cql_det.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "aaf5d2db-5453-469c-9357-c37dedea7e89",
   "metadata": {},
   "source": [
    "# Sample Workflow for d3rlpy Experiments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bfb6706a-eaec-4154-be04-e2b7c168479a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import math\n",
    "import subprocess\n",
    "import os\n",
    "import d3rlpy\n",
    "plt.style.use('matplotlibrc')\n",
    "\n",
    "from Python.data_sampler import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361d8a2f-129e-4b9c-81cd-55314fabeabd",
   "metadata": {},
   "source": [
    "## Building an MDPDataset\n",
    "\n",
    "We first read in a large batch of samples from the file. As `d3rlpy` wants it in the form (observations, actions, rewards, terminal flags), we go ahead and do that. Here's a helper function to get a dataset from a list of chunks of your choosing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3456d718-b8bb-460f-a429-9d99633145f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dataset(chunks : list, batch_size=30000, \n",
    "                path=\"collected_data/rl_deterministic.txt\") -> d3rlpy.dataset.MDPDataset :\n",
    "    random.seed(0)\n",
    "    samples = DataSampler(path_to_data=path)\n",
    "    states = []\n",
    "    actions = []\n",
    "    rewards = []\n",
    "    next_states = []\n",
    "    for chunk in chunks:\n",
    "        samples.use_chunk(chunk)\n",
    "        samples.read_chunk()\n",
    "        [statesChunk, actionsChunk, rewardsChunk, nextStatesChunk] = samples.get_batch(batch_size)\n",
    "        states.append(statesChunk)\n",
    "        actions.append(actionsChunk)\n",
    "        rewards.append(rewardsChunk)\n",
    "        next_states.append(nextStatesChunk)\n",
    "    states = torch.cat(states)\n",
    "    actions = torch.cat(actions)\n",
    "    rewards = torch.cat(rewards)\n",
    "    next_states = torch.cat(next_states)\n",
    "    terminals = np.zeros(len(states))\n",
    "    terminals[::100] = 1 #episode length 100, change if necessary\n",
    "    print(states.shape)\n",
    "    dataset = d3rlpy.dataset.MDPDataset(states.numpy(), \n",
    "                                        actions.numpy(), \n",
    "                                        rewards.numpy(), terminals)\n",
    "    return dataset, states.numpy(), actions.numpy(), rewards.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeaff27f-fd9a-4ad2-81c6-fca5f5fe6ce9",
   "metadata": {},
   "source": [
    "We can build the dataset from there, just like this, and split into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f543168b-2603-47ed-bff8-d0bafb40bb0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -7.69891077e-02  4.00000469e-03\n",
      "  5.99998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.50295370e-01 -2.41931634e-01  6.00000000e-01]\n",
      "Read chunk # 1 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08  1.47410892e-01 -8.79999531e-03\n",
      "  2.29998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  9.86362318e-02  3.75821673e-01 -6.00000000e-01]\n",
      "Read chunk # 2 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.03891077e-02 -1.41999953e-02\n",
      " -2.10001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -1.78778459e-03 -1.34615461e-02  4.84073546e-02]\n",
      "Read chunk # 3 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08 -8.17891077e-02 -1.19999531e-03\n",
      "  7.39998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.09713430e-01 -2.63658359e-01  6.00000000e-01]\n",
      "Read chunk # 4 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08 -7.24891077e-02 -1.35999953e-02\n",
      " -4.20001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -6.23311010e-02 -1.64283998e-01  6.00000000e-01]\n",
      "Read chunk # 5 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08  1.24610892e-01  2.40000469e-03\n",
      " -7.60001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -2.20016566e-01  3.79282423e-01 -6.00000000e-01]\n",
      "Read chunk # 6 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08  7.01089229e-03 -4.19999531e-03\n",
      "  7.39998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.21623335e-01 -2.86362315e-02 -8.00043364e-02]\n",
      "Read chunk # 7 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08 -9.01891077e-02  1.08000047e-02\n",
      "  3.99986580e-04  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -3.17973088e-02 -2.40776052e-01  6.00000000e-01]\n",
      "Read chunk # 8 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.03989108e-01 -1.37999953e-02\n",
      "  7.99998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.76352555e-01 -3.26280816e-01  6.00000000e-01]\n",
      "Read chunk # 9 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08  6.91108923e-02 -5.99999531e-03\n",
      " -6.00001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -1.42355434e-01  2.22081792e-01 -6.00000000e-01]\n",
      "Read chunk # 10 out of 10000\n",
      "[ 0.00000000e+00  7.95731469e+08  9.46108923e-02 -1.43999953e-02\n",
      " -1.50001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.56237335e-02  2.60569314e-01 -6.00000000e-01]\n",
      "Read chunk # 11 out of 10000\n",
      "torch.Size([305470, 6])\n"
     ]
    }
   ],
   "source": [
    "dataset, states, actions, rewards = get_dataset([i for i in range(11)], path=\"collected_data/rl_det_small.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8014463e-ab16-4498-ae33-63b91854534c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The behavior policy value statistics are:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'mean': -4.3529935,\n",
       " 'std': 2.8112352,\n",
       " 'min': -15.256291,\n",
       " 'max': 0.0,\n",
       " 'histogram': (array([ 38,  11,  13,  33,  23,  18,  22,  45,  98,  67,  88, 138, 209,\n",
       "         293, 227, 536, 582, 376, 208,  30]),\n",
       "  array([-15.256291 , -14.493477 , -13.730662 , -12.967848 , -12.205033 ,\n",
       "         -11.442219 , -10.679404 ,  -9.91659  ,  -9.153775 ,  -8.390961 ,\n",
       "          -7.6281457,  -6.865331 ,  -6.1025167,  -5.339702 ,  -4.5768876,\n",
       "          -3.8140728,  -3.0512583,  -2.2884438,  -1.5256292,  -0.7628146,\n",
       "           0.       ], dtype=float32))}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The behavior policy value statistics are:\")\n",
    "dataset.compute_stats()['return']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dc014ece-b23b-41de-b8a3-8514b1a4aa6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_model('cqlDet100model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a5dc732d-fc15-4fda-84bb-0e15aa6a3a04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16a459970>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(states[:,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6a619919-00bf-4c51-8df9-3ff598445ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1696c4070>,\n",
       " <matplotlib.lines.Line2D at 0x169c46f10>,\n",
       " <matplotlib.lines.Line2D at 0x169d0b3d0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e018182-3a94-49cb-8f5e-a2f74f03b854",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x169e81040>,\n",
       " <matplotlib.lines.Line2D at 0x169e81070>,\n",
       " <matplotlib.lines.Line2D at 0x169e81190>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cnGA2At8fsn0q/v7m5kbs7263Q7fbiV5oRAlTIwC7L6oWuusvpdcT4tjrbbK312d7u0cQBBhGgyCY0ttsYlpiS7da2mnYjLGFpxu7UURRm3psbJhcfvRhDKPOPQ+8Ppbz2N7ugXcXw33h8Z5OnJBMdlms1WppD01ry2sMbWq1Oob3Lg6u/xgAVm2L7vrLOHPvDr4vE+j+Xuz3l611OMe1eNXLptXgcPKnDPtvwzQ73P+C/0S7+8Lop1l3cXANXLdfvk5AsxnN8d5en+2utp9GNhsbHS4/+icA3Pe8XZqt+BZvNIVSabWccC/mrpOk2BwbEbrb9gy8us1Hn/xPAJy555s5cebLY9+99sQavjek2wGrFj1XX6e9vT7r6/X07+htxv5eo87M/T1c5yKN1r088IIfC6NGAM8+AYBpjllb62aOrXCOa1HkaY4dup02+zd+VRgCw+LkmX/Aeu/VMkdnMN4/izN7mJp5yOZ2j729Po2GOJSnr1PW7+j1NmP7smEH9HptHn7ihyFw2b7jSzlz7zdhGJEeMMwmgT9jbU18r3CdgHq9HslCIr/T8cQ6mv6fsX/5O8PXrdqdPPCCH2L/xq9z88rPYLBXuvfz9N48tEpjMAQ2Eq9tAAP9hZ2dcybwY8A37+6ed3d2zvHXgkY2o8P3ArC28crMj1g1sQCeu3yXU+dAhKfN1v2Z7xuGgVVbx3Vu4nlDTGuJBl+jGePD9wABnbUHM5PftdpqYSLfG3Dtwg8DcObeb+HE6S8L3/Z9BbVcJwh8DGPJ1NbQ5kBCQ6fOvj5mCABqNaHwPDfpyyzCa8Zk+Jf4/oRm6/6UIRD8eoKf01+e38hmePBuAn9KZ/1jUoZA8NvE9oa4bh+rlhTROWk4YxCIRPGps6+PGQKIjM1Kxub5MHO5cUXUoJy97xxbp74wwU/uy1XkzkYzRofvwXP7NNvP4cy934KRaBRUq/Vw7Kt47gGW1cl5UEUazphNL/LUR8U5pROnv4KN7U9jMjtNs32KekMUM6wMliqhVSaQHwZqOzvn9BKClwEPJT63AXwc8PM7O+euAH8qX39yZ+fcp6zw98Ro2Qsj/OEI17mGYbZoth/I/IxlCWPgu4PM9+ch70CcV2gklIk+DsXP85bk5/iM9v4cgO7Gx2V+JFSYy/ICGNo4sz8j8Kd01z82ZggATLOJaa0RBC6et/zR/GAwZTwQ41vvfVLq/VCBLWAMUvvK9hjefDcAa5uvyvyO4ue6/bn5pWg4Y3j4p5LfJ+Twk2vn5I+vqnz4wzHjwfsBI9MpsuqC10rGBtg3L2BPL2Ja6/RO/u3U+7W6MuTCAVtKzod2OJfrvVenDAFoc7kSx8Fm/9pbCAKHje2/xel7vpHO2ktoNIRjV6v3gNXNZRmtzBjs7p4fAb8MfNfOzrnuzs65VwOvBf5H4qMHwFng5fI/tcKvAP5kVb8nSSpkXZSCQ7H4zdb9uZ6q8rpWoTC9wz3J757Y6/o4zDASWZ6fs/8kAO3uCzLfN2si/F2FoWNs480E5Lbe++TMj4RzuYIoyzu8gevcpFbbptFKl8UqAV9E6LL2lb0nkqqd9ZdnfieKRObnl6KRzehQJBvXNj4+8yOR95zPr6p8uHtPEQQ2rc5zQ2WlU82KlPMqzopMrol90t342Bhco8iy4sp5KTmvNJcrNAZjm+GhKG3ePvXa0PioMUS8bsF9Kqy+rPMbgP8GXANuAv9kd/f8Qzs75+4FPgi8aHf3/AW0pPHOzjmFSVw9ynMG+/sHKVxtHgpGAodu5UQFoHnqK1CYwWAA29BoxiMDfRyWJRX0Crxn7+AGbObDUlEUsoImWhOHwcGfAdDNEzprA4dLUugK00ql5B6KKKuz/vJMb8802yL/4s/mPiOyv39Asgrc61+FDWh1npP5HUt5fCuAUoLhFMe+jGE0aHWenfmZyHvO51dVPrzDPbgr32kwzBqmtY7vDfC8QWj4FiW7/1FYg+76KzLfjwy5UM7LyHkwnDGbfBSwaK+9KPMztRVGdf5oyGzyUVmS+5LwdTWGyCFageGpQCs1Bru75/eA12W8fgGRYM76zuPEz/o+I8kYi8x8vaBkNMwZeMtbcmPiAxa1xsl8ftIYlLUaqMRv5GJutak17ijmtYKoJ5g6+O5NzFo3F3JbJY4fjMT8tLrPz3xf5F82cZ3ruG6fhrXkQbeRjdlrU29kP8eyhJCvwogzdiCAZudZGEa2OCvv2V3FXA7E+jfb2YYHoFbbwPYGeO7B0sbAO7wOa9DOWTurvrp9Ykxd8AOanXsxzWbmZ6pAblXJH4g8R2ftQUwz3Ym2tgR8uQgdm3YUy14YYYzFA+r1bGUJYK4wMrBmLWr17VRorI9jlcbHsls0Wvdmes4AhtkCwxLe85KHYIwATLtJq/3sXH6RV7QaWAqg2cyPMCwFg3nz3b2b9fOtWZt6885cONGUiUfPG8/FK5O/D6Zbz41CoNrYKsvHWCT3i/iZ8lS1v4Lx+SOxt3OdhgRMtKycW06z8OR0BAUvL3MKbWh34oZOjUGHiVbS66mEjo0xWPbCCNM1wTepN07lfiZSziswBnYrk5c+DnOF0I1lN8PqhSwyDCNKkM+pMLP5tUoU2ApD5KnArpPJeJ1MUymw+caWta8sp0E9J8ICwhYUvr/8PAKYdotGMz9ijZRzPr+q8mHI6si8qKcqv6pkzerUG2dy23aoXJaSgaXl3G6GJ8Qz31+hoVNdUpsJeE+NwTSbopQ1cMJzOEdJx8YYHB6uQEGXCvkqI4M2tXoaItLHEeYMVsDPdJqFkJTObzXGrkmjmS90q/TAzKlwtZL5l9hnlLfuzyfkWfvKtFuFhlXx8r3xSpKsZYbcNFUkkq+cq8qHMbMAk3ohfLm6yMeyW9SbdxbwiivnZeVcOGFF0X/5XFYlYyb2ZTIPGZfxW5dEPjbGQJ3GW4ZMu0mtMDKIErrLhnVCwNO89HGsNhJpUs8wPjqZq8hRqBDYblEvaEKnFNgqPDDLboomfwWJYWtBbzZrX1l2sdNgGJbsBBusxOMrU2CWVT6XVeXDspvU6idy8xOweJSV+ayKhk6NbVk5F3JXMJer2JcNAf1aszoEhP2jFMVlfHVFImV0bIzBKqjubhR2mTSMGobRAAICf5r7uSpkOk2sxIGe1GdWEbJqyrk0Mgg35hLGp9MI+RUaA6XAllGWLaGwTLuJVSvu/LhKaMN0WoVOg+C3AqXSFOPLcxwiXgqWWoKX7K5Zpix1fkt5z/IuEctuFXauNcwmYBIENoG/RDGixi+viAJWNJc1k6BmYvgWtWCrUKeo9vG+dxsmWhltbs5/PFtRIGep5p0o/exKlBjKm01XYujjWMlGkUJgVooMlJAvIQgdJXRN6vUiBbYCqKFuEdTA9GvUjTJjsBi/2L5qRcq5Vi/eK6swPoG2dkXGpwqvUvloWAQmmF6dmlls6BaNsmLUjvZJFlyqyDCMaF/648XlXON3ZDCRDha0hVJpBulcT0zGQ32yghxFCR0bY6AaYS1ELeEV1bzyMrmVeHzIyMBKtw7Qx6FaUCxleNrKU2+WtipQxidYhJ8jsPFgTfAz7VYIc2VRFWijCgUSGap5xcZgUX6xfaUrMKt4r1gr8Z7lvnTWc0shoZoCqyIfai7rbq/wc5EMLDA2dd94N4ogrZLyVEvjN7ecS3ZBRxnyTgg9ZfNaIhqfysilWcNXc+mnDV1Mxm9HBqsn1al0EQqknNXdcq8jXLxFFbQllsRyGpnKWR9HiJcuwkvlNJQCc5phAjyP1Ni8RTbmgfhOcFoetXfWS3BnNY8LCJ26la5dx2/KSiK3BHJbEOeO7SvlqTsVDOsy3rOsQgnWpTEo2ZdVPPVc+VDXvdatcC5rbsWxLbJ2cp9wRozJzImQY/xChTmeX85l2XHQE2tXczdyy51hSUPXl2PbbBO01FymxxaT8duRwTOEpML05cJZbvndpJHCXHDxFM7tNDIjgxgvawmvYSwv/94WzzDtFmapAlvQ0Hm+uLDHAP+UMnYlhmeZyOCmFNSTXfymUGY17wgVmAr/tcjAymjVkMlvEaWyL36jd6dUYE7xvlwqt3RTfme7Hc1liRxYy8CJe/I7dwklKSLWXuFXFp7LIAj5eXfKyMApbmex1LopQ9eL5rLuVIvGF3LA5qRjYwySbasrkbx+0uuJ7H7NLe9SuHTOwDLwaw5GYGJ5aYWpj2OpKOSG2MzB3WIzWnaz9ArGhWGiw6lQmBstvLbwxGplQrdM1cYNzRg0FL8y73kxLLjTaYd3VgcnlGFtFkINgt+CCnPiCENet/C2pZPiFPPS90kQZFfb5MqHMqzbXfymcCBqdgm/ZRSmMj539wAJE5VFrFpZ8FxyPnEEdNOs4fWkp146lxGvuSsGVWTQa+E38+UgJuOrgIIr0rExBqY551A9X3hgBjin5MUrdvlGW8pbV6zrwghZbpqfPo6I1+Les39WYGCW3cxsBBbjvejY+pFH5NWnkl/xXC4XGShvtoPXEPyOSoGZphny8++UOLfTKoQaxPcWhBv2orH5jWpzaRhmDErJ/j058qH4nejgN+S+dMrWThm6BcohJb/gVBffUk5RtUjE98bzyflNfS7l2Mr2pVkXFYOBRxDMmZ/oywrDXhtXyUGG8YnL+OpKrMvo2BgDdUtWZdobC2+218ZtioUwnfwknaKwDnlBjC8AfGkMTDutnPVxmGZL8prOf3hJCoJ7CgLDw/Tq4BY/Q+c3F+lC0BC/35yle7HEeC0TYe1HCsyry7Wzi5vPLZqoGw5HEdRwBwQEWE4jSoTm8ltwLjXlrAyd5RTPJejzmc0vVz4046MMuWkXy4Ea29zl1bYr8j2Wgd918ZqSX0kaQMfx55LzfX1sYt3NeeZyXgWtO0UN6WDO0nMZl/HbkcHTT3sZGyVDOSdpYW/dV4o4wKsLj8OYFtdNG4Yl66yD8N7bSmR7ArqxDPzuDL8u8wezYn4LQze6ENSkMXDqBV9IQhtzGrp9yW9LU2BOcU9GdSBtbuWs8XM3ndCQM3UKv2Isyk/fl5ZyUornEpYwPsp7PtHFq4l/W26JIV/W0G118IJxGCGrhHk+P7VXFhzbrZrLLDkom8vQ8Nw2BiujRqN8kWOk4c6hdzmtYAzMBRfvQOLO67UwZCXDGCTHsZDnoBJZm228YIBXk+FumTEIFdiiMFELr6Y8sOK5jEEb8/JTHl+vjWeJ7xpl/BYU8IZlCcMKeB0br1FVgS3oPYcYfgfXFIf/ysYG0fjy+OXKR6bxKTGsai69xQ2d743x69X2pb52c8m5xs+tCUirbF+CbgyWgEtDOUjPpT4Ga5mqujnp2BiD5D22pRRulC6utOKGXd4ScWF4Q3qXQc8q9IiS41goZJWGh80WnjuYIzJY0APThcBSkUH51jNk7fxcUc/MFQnWmglrTU2BFfOLkuPzja3rWwISWm/im5PIkFc0BstEBq4ljEGVuSxT0Jny4frCcTCArTauJRRmqWFdNOrRMXx/jF9T+7K4xUQ0tsl8cq4bOlP0/jEq7UtpWL059qWqqANRSGHl6xR9DLcjgyOg/f05u18qqGG7HSoUo0K+qCxRl0tyY/obRB5RBtSQHMdikYEyBm08b4CvIgO7LDJYHiZyTSUERGcd8vgtYnw0XpiGJnTFX1OHtvx5DA9wePF6yM/3NAVmV1RgS+QMXKXAZhWclBJ+mfKhVYEFpq/NZZlhXTDqiUUGo2hfljopit9sPjnX59KQc1nJ4VsgMjiciblcb0LNxA11SpqfPobbOYNnAsWgBukRVTEGi5aCqdrxDT+CGkpyBrBg4jOEiVpxBVbVA1siMvCNMb7pYgRGeCo5n59U0PPADSHu3BZ3KMtwHLuq4Zlv3cwDuVZJY1BRgc09l1o+xDWl0nAoNawLwWB6RKcpZ2MOQzdX+aXityXnsmrEumgksh/lKFzjkMDwMVyEF1/Eb8m5BPBkVGeU7ctlKgbnpGNjDExzzlsvtI2pPDCc8o1tLOhhKn7euqtFBmkhSI5jIS9Fg4l8vzo2u5Dh8YOI30ZrYSy4MqUUilRgJYbOkDdNBf5sroR1bWCH/Dx/XNmbjaCGOcZmezCywTJgrYkfDGX5JeCUKGir2FvPlA/diHuj6utmWFHDxnnKL9U+6cm5rLxPlMxNq8t5DE5s4PujBaK6OWRcO3AWBAGuKctuM4yBPoalugzMScfGGPR6c1y/5wdxr8iQC1eiUCAess5FUhC87kzblGkhSI5jIXgjBhPN4c0uEvUMZiGmTt1KQCnVPL654Aa9kmiOsRmGuVCOoq2EeTPhzZYplEXmUimUjRaYBr43WSCqy+aXKR/7ujHQeFWKWCMFXZn6OfwqOimBP60u51p0jGHIfZnvhOkUJePnWDttbIE/DavOspwUfQy3I4MjoIODOS6HGM7AC0SzrEYNzxiIENILKoSQCwgBhMkltzMtFPDkOAxjgSRrDCaazIHNLqDAtH4s4rvjwsgnzm+ByECD9/w5PHXBb/4chX3tMOI3lwJbwIhr5zVAnF4uyi/F+RV7s5nyEVPO48q5JQBj3igyCDLgy/lyBr4/rS7nybn0dUN+FGsXja0sgtTHYCwShSxIx8YYeCVKPEa6R+TbBLjVPUy5Uea6rlGDUtzOqNBzTo5juchAwUTzjU1AKRUvEdHKSoG5cPWFIDctMohBUhUU2CJzaWjQxjzGZ9mSYPXdqtBGGeSWKR+6MZhjnwCY1pxOytgWOaRWDVr1ufjpDlhlOU/O5UJO0TxRj7ZPSqBSfQzmIhV1C9KxMQZzUT9KQiph9ety0crCcempT8cPV/eKRrYwCJ06vjEmqCjgsIDCDIK4MZjDmzUMU2LBEPgVseBk4myOnMFC5Z7JnEHFsen85lHQ5qFKILfmSnoai8CJB5FhDYJgrvyLXn5ZmfReOl71iE7wm3MuE556PBIpM3TqO4soZ2UktbUrhYlUYcNiMFFqXxYk2YW8GeLynqoO2IJ0bIzBXJde7McXDiBQxqDEwzS0vvKz6YVq/FT9cYjh5wt4chxzew4TRyQbm7oHNg+UMqfxSRiD+bznOSG3IEjARBNtbNUNa2Xj4weYKoGchFKORDlHClMkZj38mnJSqpdfZlGmfPSjKMvT6/4rOClzQ3xJOHEBQxf40+pyrkUGQeAR+LOFSlkr00FCp5gBfs0T5aaJ5L8+BsMwtOKGJe5kqUDHxhjM1ec84V0C+DUZulVUYDCHIGiJwdhhmwzDkxxHGP4HFcenRQWQhG3mUJhVq0T0xFngCqGrz6fAKhuekYQa2nVxl4E3xrc0XlX7BVX1MAczDD+AtYZIjs+hMJcqT9zUnJSG3Jdl+ZeSqrOUfGhtx0UVWAJGWXUpqxb1CPbjyglrvbS0spwnktUAfl2uWWlhw5xRjx/EIhF1mjhQ/BLjS45hoeqlBejYGIO5bkAKI4OOJnRy85eWKEbGoLKHGYNtipVzchxzRwYaxg3zYaU6v0Uig1DoGtUMa1kLhRQdRAIHsj20GUSGvLT8ck4sOOHNzlW9pBnVRfIvoZNSl/uyNOlZrFBS8pE4JOV7YwLLJ7ACodxKz4jMWTpbBBPNkUCuLOeJvBnoMr7iBPLIFsa1U4dGTUMbsvklx6CKRBbqmzUHHRtjMBfta5GBn1i4ipUGAEFQXOER0mGOMahUtTHnxtTLE0lgpXPwq2x8EokzyBeCJM0NE4Vji/dzqSrkc8NE2rrBfNCGCP/n9PgSyWqYZ2xzGtYMeA/mWbsFYSLdGFTclwu1LclwUgLVM65iaemiY/NSDmY1OTjqJPKxMQbd7hyXXmTARDTkQZCKCgXAr4rxJat7CiKD5DgWjgxiMNERRQZBEPNmvVCBqbms2nNm/ghLfE/xk++vukpEXmrDupqTxXD1SgraD2K9bcJcQ8V9WaacU/KRUs5SYTYlv4pnRKob8sTa+dWjLH1snU6rnFeiT1C0T6ruS1XYUDU5nmNYlapIjC9PxitDwQvSsTEGZZeNhORpzbk22xkKrMzjizoOVk4OJmGiAo8oOQ6VXJrbu9xsEQR+vDyxgjGIqokq8Btmh8eVDeu8sE2OMaiuMOeMRFQl0Yaq7lkUcqvgNAymwiDI/ES4L5tmJX5lCeuUfOQosHAuq1bcLBUZVDwEpp14Fr05SmiQgMDU2KoaumWLKELjk712aRmXDt88jfEWoGNjDIbDiif41EZZU1ip8sDkVJWVuWkL6XmDajz1bobJZmeJRF1yHGZ46KxqFKIlIaWgVvWcYU5YKkcIqKzA5oRtUnX48/GbG3IbyM+tN+Vv9Oeby3kMq4LbNvPmslouKy/xn5IP/YQuugKTHUvL1m6Jfan4eRVLSyHaK8PB/ty8vOTYqh6GrOzsZc9lqFMSa5eS8VsUGRQ3Jp+TdnbObQNvBj4buAF82+7u+Z/N+NzXAN8EPBc4BH4W+Pbd3fPlEnTUlOtdVhMCncqukQxJD1n3xmD6BJYhTjy7PtTznzN/ziAehQCizBQqttuYo5ooz7sM+a24migXJrIAr7w+PhDv37zyc5w88/fK+elGPPTUq40N5qzMSh3eU06KUmBlJ5BVBDkvdJk4SdyUczlHgryUbNknyBJ9goIgwPPHGDUNUw8CKIjuDbMJ3oCgisJMzmW4L6vJeFjqWTUnWCYHZRchLdoFdk5adWTwo4ANnAa+Enjjzs65F2d8rgN8C3ASeBXwmcC3rvi3xKjZLL/ODogJOGQsXIUka6N5F0DpBfOA2ORDsYGDNUt6iSY0svklxxEp5wWMgcQ8jQUU2HKRwbzGYFGYSPJvqbksNnYHe78LgOvcqMZPiwzmHRvoCrrCXB7kKRQJS5ZFBiWHBVPykZhLb05DPhd8qR8AM8QBKwJPNJGzzErVS4pfvcrdNqnKJSUH8ssVE9bVYaI4Py+5VxL7Mk/Gj7q0dGWRwc7OuS7wJcBLdnfPD4F37uyc+xXgq4B/rX92d/f8G7U/n9rZOfczwKev6rdkUbtdIbEE+QqlotABdDdegX39qWpemO2JjV638C3hIZhWWyjoiSP4aTYlOQ6Vowj8Cl5KMglpJxTKHC0bKkEbOeF4yK8i7rxIiS7oCqwOzCrke5qF76dooM2ltyeeoQS5kmGd4zCRUigbCcPaquaklCmwlHwknaLQsCo5KIOJ5hhbTkRnmh3hrY99wa+RHyErfs1GBf82D7YJPfUVjg3ynaJWHXBT/FIyfouqiVYJEz0P8HZ3zz+svfZ+4DUVvvupwENZb+zsnHsD8AaA173ui3jwwQcBMWG1msVgIC7cqNdrrK11w4shDAO2tnocHg5wXY+Dg0PuvfcuZjM7PNTR6bQxTTO8gLrRqNM9mGAAkzrM+gehB2YT0AX8qcNkNA5rgbvdNoZhhDifsOpCYEbDQ6zmIZubGxwcHIY9R3q9DSYTURNt7k/oAUG3zt7eFfnb2wQNCwPoX9/HsFw2Nzfo9w/Y3z9gc3ODra1NRqNxOH4/8JlOZ4zHYuO1Wk2azQYHByJvUatZbFAHP8Dv1OkPBjTknbauWSMADMdnMhwzse1wjuv1GoeHw3CO1cYcDvtQ67O11WMwGOI4YkNvbKzhOC6TyZTu1UOagLte53Cvz2h4UyxqSwiTO54x2O/H1gnECczZzGYgT/d63gzbduLr1O2Ea22aBr31dQKpnPfdKZteE9cRv9sxDOqAO5pyuNcP16ndbtHvi6ZglmVi1dZwnWsA7O31Y+sEsLbWIQgCRiMxx1uHUwzJb9oX3zPrLQJTQHx71/bYOrXFaDTGth35jC6+7zMeT/Bcobhcd8qe/F21msXGxjr7+/0wXbS1tYl7c0AdGNV8Go6DPRPrakvoJJg67Mtn1Os11tfXwmcYBqyvSWPgCV76OgHMZjNOntwO99OWlIN9f0aw18d1BD/XMqkBo/0BLc/LlafJVKyl78/CsZmmQa+3GZOFzc11nGsHtIBZy4LZDNeWfb2MFn7Dwhw79K/u4TttLMvMlKcAYSguXXqSe+67J7ZOzWaDVqsZysLajQENYFwPmO71GY1EnsGtiWe4oxm+7YTrBHF58lwxZ459mb29fUS1iVin4XAUysL6ehfXcWntS53SNKi7LvZM7DnXNGgC04Mh471+qLOeeupKeNvZxsYavi/U9GCwT7M7nUvvqTm2rHLIepXGYA1Idmc7AArPh+/snHs98HHA12e9v7t7/k3Am+SfqWOP29u9wr83NiL2lmXR6bTpdNr535EeUft0j3Zvk8ENqeQ314EJpuPR7XZS1+ttb0eh3WBPCF67bbG5uQEQ/l9R+IyBGJKx3mJ9vcY1wKp1MSRM1Gt1QH6319vE94Pw966tdTGCNfYugWkYtFpNWq24dxsb28U+AGavzfZ2j4H0WJqtdRGJzFzaVp32dnJs0TOGe2LjdzottrbE6+vra7HP12o14d1MxGasnVhje7uHMw4YAIYs/6s5QfgMfZ3E89uYnODmUwAOjUa9eK37E9HXf73J9qlt8Vog1rK50QUG1Lzi/bJ3aS31etZaN5tN4YnPPALLYOvsSQYHJnuAWeuEUd12dw0Mg7W1NFzYajUZ3uwym4BhOGxvxX/XVuLvupzL7tltqNcxzZkcm5g3Y+alxqY/w/Pk3ck44efCdUIYv3pdzvFMjI2aydbZk2AYXHtC8GusrQH7dK06FMiT6fc4uC5yBsnflZQFayaUevPUBjSbBJ4wnLX6Gma7Dv0pvXYHtjfznyEb43W7DRoNGSU0c2RhIvh1zmzR2e5hD32GQF2uU80PQN5DnCVPnmty7Qnxd7txhfbaC8P3k7JQdwJx2LFh0d4S+8E0hXNR31gHxrSwaGlz1O12YnNWb4j9125b4XrNo/eq0ipzBkNgI/HaBpBbUrOzc+51wPcDn7e7e74iULsYWVbFoSYOEkXQhtwUq8aCZb6AbiMMHy2rEyWzEuF/ehzSM0zbyTQlcedYOF4d665MOeGx0ZRh8KwscbY41AAart6Syqr0voY5BGig8jzNsB8+yLlszYerVxpfDmxjqLGVlUOGp1izecX2lT6XMvKIoA0lB1UvC5p/bN4C+1LxM4wK+zcnL2i05N6pCF+K3zos5qXnCxJzaSg4aFYs438dD509DNR2ds49V3vtZeTDP58L/ATwhbu75z+wwt+RSUlPIpdSh1+k0LWVQqnQv0dhilUqKUbyM2vNuEJpZCeXkuMwpDEo6xUDxG4cg0gITKtduZKCqvxiB87iSU9DeZIVsdlKuZcEDgya0FU0BlZtrfD9GEmFYibyIaalrV3lcw1zGDulMNVctqvNJYaFEHePIMhqgKjtq4SyDAJX7mUzUpil+Zd51i4nZ2B1tFxdNX6dToUMckb7atBlvPpZolIZT8iA4KfkQEacibVLyvjCV6TOSSszBru750fALwPftbNzrruzc+7VwGuB/5H87M7Ouc8Afgb4kt3d8+9e1W8oosqXXoSCkPCeKyoUAA+JiXsVBEFFBmsNTTl3cpVzehyq3G4OY5BIjs8XGVQ8vDdxhCFr1kTjOLS5bHYITEOUzbr5VSJRCd88CkUZHkeW/lkYrWpRXaXqL0UyMnDaluSnr11FbzZ0Gko8PtcXToNBdNrZSxiDCu0vioxPbF+lopDIaTAqRj1zVbmlPHWxL2MRcmntv8rTldwj4niijNU0xOVVRIbcaEnlXKGVuyLPLYkMDtLGIDTkoTGIR8ipC6z+GiaQAb4B+G/ANeAm8E92d88/tLNz7l7gg8CLdnfPXwC+A9gEfmNn55z67h/s7p7/vBX/npAqXXrh+ULIDWAjIXQVNwrAf7nU4CuAn7xk84/vhJNFVa3DjMigwLtMjcOYxxgkeqTEIoPqZw0q8cvwiCLvuUvQmGBMXQEV1bKrePR7iUspN+rpYNSqVS+Z1jyRgfhNXrdOnciwWgWGPEmVK8H0themghokv6Y0YLYnqsUK7gA2zAb4EzGfVjwPEttXhU5DRU99CYgvJgdVq6UMpdhL9opWDhzOpdorjQ6Ye+KWQ9cXpa0lVA4Txc80iHbZMqpsybWbFsu42id+1XMNC9JKjcHu7vk94HUZr19AJJjV30daRrowDbVj6hK3U16K2ZY/v8JpyL+aNKAFBDZ/cgiff7KEJ8BaIxsrrcCvMiUVphJy3ZstKy+tGBgUhcem1SFoWkIIpi50s41BiHNXigyyD2VZVic6tFeiwDpr0ZGYIHAxjALxkAo6WJNwyCL5F4W4lRnWhOcMmgKrd0XJpe2JtWvlwyRqPFkwURE/T496ahUNnazuCYIKTlheJKLDpaU4fsWDYFm5pTBX1xXGZ+yI8dXKzyb5YWI+h1L3NCjD2sZoy+dXPDB4+z6DFVGvVyFncJAhdGpjKmNQITJoWGLxGthcLHNqwwRyMw415Jx4To9jGZhICV27Ms6tqFSBJTwiiBSYZXUwlSAU8VM4d5CNc8codWJWG1tFQ7e2+Wrtt5YInvQwmyc35OcXgImqil/RvjTbWsK6zHHI3yuxfVW0T+YuNCjZJzPpENRM0cOKxFxWTcZL77ndKimhLDKsc6ydomb7geIPJA+4xcaWHbEmZXyhe84XoGNjDFQ9dSElhCDw9cSZBhOVJE8dmTNoGDaXy4x5mEBOVhNlRwaVxpFHqVOs80MblUODhBBA3OPzK/SBid/yVOLx5VSIxAS8tOGZgSUriko9TGkM7Ga8QiSuUCoq5zLvWWuIpyiMWGP8ymCE/LWL7atCDL9q1FPRSdF5ye94iySQ5T6x1UHKKvwkRRFydadovfep4h9ld1HkdH+NGbrEvkzKeHiau+qFUgvSsTEGlS69KFAoRt0CyxC4bEHSE8BGWPImNperRgbJaqIcoUuPQ5aWllX3uL7IT5iGaMLHYtCGUdUYZApdNJ9+vVr3y7CiqEwQUt7sKOQ1Tz6kMtYt182uCxGKQXytisp5XoWZAW3E80sVIcWMvRLbV4XVPSsuQS6AbUQkUrUxnjAajlPSPK5oX85TFjwv5JZ1urphCfF1PJGvlJS+wOo2THTrKVm+py8clMINF6bwbY/CNTeCia6Urd9QiwwqVBMlyagKE6lNmZU4O4oEcpYC08YXNnQrwUsxFPZcMA96tU2RoSuBiUYeXHHq8pElv0uuW9CNQxvWXMq54hmRlJOiVUoZjcoKrHJUl1JgEc5dnVdFKoBt5olEFs0ZBEGgRVnVYbBKlW4Z+zKWfzGMSu09yvpKrYqOjTFYW+uUf2iQFxlIqKNAYc58+Jz3w/dfADsQC9wsg4k8X5RgGkC7ke3tJRSYGseBC5/3fnhd1RMaGSVuvlbds/LSUgVtrKu5tIVCl73nLZU0rmrsiiKfjGobbwGY6A8P4NAXgnfukWqRQfsOcSp2sShLiV+JMUhVSkVQg2EYc3vrWcYnlA/bFXvSMqGjzgosEoVU3CeFkUE+rp7iJhVmo+yYQfIMhT8FAgyzKboMVy4LVk5AgfHJqlzSoWDIhIqSuuo2TLRiKoVRQNuYce8yXLgCPPEXr8GHJFxpE0UGhcZA5Qu6DTCNBFaaLXRqHN/3BLx1D560K8JEpeF4sfc8cOEbPgzvVefJy/iFQpeYS1MosKCigq4EpZRAUtS1cNzPf07bBEdeSPCOfTsfDfQDGMvIoF1L85sXSinNGeRV20jDXhHnLrrgKdw/B9q6JU7MpqKsgrnUnlz8dtnaVW4rXUE5Q24zw1T0X/FcQ2FkEDopOYYONGMQ/e6kLJtmC9NaCw+fHRUdG2OgmlYVUiJRF6uvhtwWEQA/eVn8f/c50K2pnMGMq3aBzGhnDAS/SGHmKZTRaELfgR97SvwdSA9s353Pu4REErLkvoZveBjeeAl+r19BOftBQZQl5tIx5ffLjEEVKqi2scyO8MoqKkxbNhkkcPIhvpEtht+pM5JN2haqSDEqil8StvFzvMsloJtQPkrgPTGXlrxUrCg6WMJJWWAuVW5p3gRyLHkMqz0jokcGkpJykNXBN6mr2t3n88KP/S3uf/6PFP6mZenYGINKVFSRArkh8uUZ/F4f6gZ8zRn4rZeLjdk2bNwA9vP2ldaXKMWvUWB4rsDAg0/vwb+9TxoDp0zo4vXOUL1k8MNj+Jmr4t+Ky7TImZ3Y4uBOux7W+Me8PfQbsxavgAmprNoGKgu5MgYNHJ7KS/4PM4R8Tm/2wI0MeUDBZPpBSqkk57KqNxtRUZSVnksvj18Rzr1EoUF2hFwNw6coZ+D5Yu0yTnJbFfOCit49EPvEK+JXFvVAZmTwdNGxMQall9tkebPJcDxnY/7idSFef/sEbNWhW1M5A/G5a3keplZJFONXIASNRoMfl1HBP7sb7pM/rTRgVx5YL0dhFuRDfvwp8fyvvxPukvrvL4vO2hwohZKhLKXQWWvyd6xEgZVU20CpUvnoRBg9O4jKgp8sMwZroq2xuksaEoY1Z2w/cwVOvhN+6kqFfMhwJvZmt5E2rGbCm81RYDds+IVrkfHJolA+lNOQd6YBCqMsP4AfeAJ2L6pXloEv5zAG0lM3jYJoRR0qXdMPlS5mWH+rL+brA4MKkYEmB6p7bAQTpSODyhdxrZiOjTFItqJN0TjDm015l9kb8+ek1/wVd8gXTPG5hiE2Sq4x0JrUQR42G9/cfzBp8vAEzjbgC0+AWbWaKJWE1BO69VyPyPbhf8jx/aOzcIcsCS2MDAYZ3l5C6OrKGKyiMV5ZuSAUKpXf34cX/Al8/Yfj+Z5Lees2iIxBq9WMGQLDMAtbKAxc+OaPgBvAxdmC+ZC8fZmhwPYd+Pj3wN99CG6EeivNL5SP0LBqEaR+zgBy6+MBfugi/KvH4Gev5Q8pRmXVRCUJZD+At+/DVBZtGGaBMSg6vFfRafixp+AVfxoVibz3sEJksJ4eWzoyiPiV6qojomNjDNTFFrlUFq5CZvj//iH88SGsWfAFsu2E8lJqyMggb79orSgmHthaQ7AsXn0HvukR8e9vvEu1TqlqDBLH4pWAm8UVKb9yA2468NIuvELv8FyknCsI3ciVk1IQHgsWFcovE8lqSPRdgtzx+QH800fAlo/XYaKbuesWGfGDg0FaoRR4zv/pScLn+oH0Thc1dBUU2A9dhMflI4ZefmQQysdBei7T/LIjkYEL3/9E/LmF6GVYuRQ1jQuCINEmpRhy++ZH4NPfB9/6WIWcQVEEWcFpeFcfvvFheO8wchpu2jZe3hgPsyLkPMMabbZSXXVEdGyMQSmVVdtAaqMEAXzTw+Klr7sTunLfqgMpoTHIhYnEG16nyZc/BJb8vGHUUwrlqg2v+XP4yMzihR345/coXnOW8IW9e8oNHcBPXJLjO6uKSyoYn4zIIInNBgXdKG0fvuqDAkqZ+lVyBosrzF+8JiCvE6rcW4OJ8o1BZMRjvEoqUg5c+EEJn/yfl8G25PmRSUGYlahwA+KQlM4voZxvOvAfnxT//oye9kYV45MRGUT8sr31H3sK9lz4pA14Tc+Qv6Gi06BubPOngI9hNIQcqUow148dzAJR2fZfJGT6lF0hZ6CftUmOLbVPklV88G2PRX87sq1bHZeH8+xPpWqiaqWzt4KOjTEovdwmQ6E4bo5CkdDND16EdxzAqTp85/3Ro5QxsJAwUd7+HAml8kP7DX7tZvSyHxjC7TcN8AICx+dr/wr+YgQPNH1+82WgWrBEoyoQOtX/pW5q7aRzFIomBL96A357Xxi5rzwdDi6fj6JMjyg+l0Y7/7DNdz0OP31VKJbc5LtOZdVEkDk+L4B/97j49/c+Cz6tp7USKYwMIpjIsszoAqRUEjKuUHYvQt+F1/TgM7bg2W2xevtFucN5EroJhfKDF2Doweduw0+9EJQhH2a4sqF8VMq/pB2HkRcZuv/7fvjCk4LXYdH6ZfBKwokURK3fLpXzK9ajiK44oTvLH1tJXvCte/AHB8Jp+O8viOeW3pvnyGfkDPJhouh3q7W4OIV/+CEB8U1W2K8yj46NMSi93CahwIIAfvmaUJj7Xjr8f2IK/0Zuxp94vkgcK4qMgQsEuZHB7FC88TtOkxP1SEA/NDFiQvC7V13eugfbNfiDV5jcp5Ubq6oNo8gYhEnB6LalixOxKSdkQxuPTeBr/0q89D0PRJ5zRBW8ywKsdP1kT7yRUGCPTSKlAjrMkMNPr7bJaz4Gmf17fvKyOBvyQAtefwZ++kXQqSmYqEpk0GRzc4Pv+6jg9ZSbEYVIL/x9A3EgEeC7HhDLYCrDWnTOoDAfkm/In5rBf5Ze879/AO5uheeeGGQo6FA+wsOCkQJzKyjM739C5CReuQ6fvQ29sOHrcnBiHr/f24ff2oMNC9760kg5f2C8ovyLxssPIsPzbffC3z8NjgYnvm9Ywi8rMijIGWxubnDowqvfC//1skj+v/ly/rBWRcfGGPT7JZdeJDbKL1wD2xUb5aorF1PziL7ncaGovvI0vPZU/FGGYYZtFGp4uZHBU3uy8+V6g3d9TLSJ/6Av/yHLL3/pSbFR/sU90J3Ex1EJJUpAREEAX/OQ2JSP2RkldUHANz0iPPPPPyGqlpJUmKHI8PiuTuPh8cFMetMJD+zco+I091edhh9/nl4Bk8NxJA9ydKLE/74DT03yjIFQmH85hG/5iHjpe54lgqa7mvAVZ6SQGzY3c0uCpXVfb/KWi0PeuS94XXWlsqyZolrFD8Dx+cgY/vZfiHG94Sx8ak98LBpZdQUWBPCr1wS/QZBt6GY+/L2HhLf+2pPwygoNe/v9AwHDJNonXJzC1WmxwvztPfi+C+Jr558j9mSlNikZkNSFsRjbDM0YJBTm1IN/Idfu3L3ivpCvOCM+4/gu0zwvOizRjfbl/7mRcIoyjMH3PA7vG8LdTfiGu8Re+eHnin1Sx8k+WGp74vdaRtiNFWDolMNE/f4B3/FRYh2P/8OF0pZoS9OxMQZ+2WlJTegGLvzzj0TethNIIyA3ijN1w7r7//v+7Mep6KCOkxkZ/PkAWvIU6397RZPndQQvPzB4p9L3kt8fXnMxga8+kx5Htcggrpx/5iq0Ea85SMEwDaFMA/jjGx6/flMkxX/yBWI/R/wUVahVl0J33YafvxL3nj11x4AWHv/3y/DL1wUs9X3Phq84HXE8zMOeM7yvr/sQ1ILkgcFIyJ+awd96v1CWf/+0VgUG4R0KVWCiK/UG/+jxDpY8J1A3tfbJ0nEIZi5f9yG4bMOnbsKPPCf6iLoxax5j8EvX4bJUzpecbEP31X8F7zwQFWdver7+sHzPwdcjrLWofcLrPwRt8g3rw2P44g8IyO1f3Zs2dIWUPA0cwHc/JsZ2xc2ODFxf/KY/H8J9LfgW6ah8w92Rcv7t/Rx+iUKDt96EDw7F2C7a0iAlDvD9+FPwnY+L8bz5BSAvtuNUM3IabmTtEz1vJj22393LcFIyDgz+ycDkPz8p5O69HwfPa8OTM/LHtSI6NsaglDSh++7H4ZINpiGEfKCSmBJKuTr0mPjwCRvwvE7Gs4hXFGUZg/NPBJycCeG7Y7uBUq4BmjGQ/Oqux2dti1A/zaeCB6bVju87wtCZSPfJSCuw3UfFezt3w6lkyXMIbeSz04UuCMTp5Q7iNwx9OWF1U7aIEInBn7kC//DD4q0feY7w0jdq0fhyS1kTUcjb9uF/3Yj4ZZ0s/e7HZUK+B29+fjy6Um0NqhiDv3uhyVXXDA3xTE92S4/vdy67vENizW95MFImEBnywlO6Gnx50xGVTw3Ea640XLqyfPu+iGrXLfiNl8EdGSXrucYnkWD9pWvwu/tCwUK0p3V+3/FRGPvwZafg/3lW+pGFRiFhDP7jRXhCRgZTso3BuUfhf14TDsP/fgmsybfUutVx+OXrOfy0/EvfEaXEbblPXEMKlwaXPjqBfyar9/7jcwX8FY7LUPzc7H2ScIgOXPjyh6BrJE6PJxo2vncAX/14lwCBBHzMujjICvD/XskZ14ro2BiDra3N4g9IQfhIvcXuk2IT39UQQvPIWG5puXA3hsKKf9mp1FNCCiMDw03BRIeuUBKNIMBv1EKPHIQxeGIKT06jips11+Xr7sweR6VwPISJ2vzrx+C6A6b8/FgvN5Tje88Nl7oB33JP5siKuTleVC7YafDz1+D/ux4pFIW1bm33QoX52L7L135I1N5/+33w9WeT3CoosI0WY08k3ADM8FSviuoEr8HQ4c2Xxcb/8edFifiQnxE1GRx4orIpRn5AIM+H/KHb4EWdgPPPFr9t3zWiQh25dj/5hNgr33k/9JJ5lzJDHgSx8X3TI8KIqbWzg/i+DGZumMc6dy+8bC3xuIK129rajOVe9hz4xkeib4qfY8b4XR+4/MI1aJrww88hduOmMuKV4MSNFo+M4d98FCzppLjoTorgd+3Q4UefEmv31pfCy7VS51A5Gy5/mnXdeRA/VLrzEZFXUXM5C6RS1pyGf/dRsSe/5kwaKlUnnhuURAbSsP7ThwXsqoyPofoMaTDR+wbwKe+Fa67JZ22JXB3AP5DG4PFpxXZQC9KxMQajUUnPEqkw//m1Fm4A//AsPLctZv4DIyV0YqOMJ0LAv/SO9GMUKWPQNBz6blyp/MZN2JQ9bcx1WV8tN6XSD+86gOvyGQ8YLl98MnscSv6Mok0ix/ZwvcWbLom2Gd/zLPGFPddgpDBWKXQd1+WztqLSx2wqh22uOAbf+LD6nXEFNhqNwxD5Pz3qhkKX9C6DxP/zxsZGk3/3UXhsCi/pRsbgwJUz1FaGx8ENBDz0gm76cUrIN0yh8PeSgj6yMQK4Wa+z3jD5X8+d8EBLKRSDR1RbGelhPnHgsmbBP7wzzSvQ+iBl0tQVxrVh8ZbDGj97FTomnJZOyp4jxVfOYzB1+MODgBP1CD7JoqxIZDQax9pefOtHhOH5lM1IYT6pIly5Tx49EHLwhjvTUWu4LytErP5Gm6//kIj+TtYVNKupJnkG4TcvOjhy7T65l+BnRJHBhayT4zNX4PgNi98Y1vjvV4QRu6Muo38lA1I5BzOXn70SUDfg392ffpzi1zCcbGOgRXT/86qojmub0Vz6ythp1UT/+jERZX3xlsuvvVQEzwD3tuCRV8Efv4KiK66XpmNjDGy7oORs6oDt4dctfnVUo1cTpYZbNSkEM4NLMzRl6fEJG2KR8khtltM1ITDXNfb/6wactOOnj5W6U57+uw7gr1zB7+9seuHd3MlxmHPARD8xEpDJuXvhedLQeYHBuxKw1JrrSrw+c2TF/LTQ/9yjwhv67C14VlspMCMah5zPt112coWutOGZvEnqeqcdRnRvfgGY0jr+2UBOnEziHcj2AV+XoZwhaoW8bonPpZLIEiK63mzyT++Ck9ihJfYDM5rLVjSXn38iHYEIZrKFAjl7UxrWYKPFP/uImIfvezbcL43PB5VfULegbmJ6AW3P4+/eAeu1rAfmH+CzbSfkd7nZ4ievQMOA//oCMOT4/qAfNz77ci6/JnMuq0esv2S3eMcB3FGHb7lbKOdDV9N60hh85KaQmX9zXwY35XzhMPQySlq1ufwG6aB8zwNwv0QRPzyWYzMNaNcxAthwHL74VPQZnVRjvCY2Q4900lrK3GitHfLbfU7kpESGXN6fMXH5rT0B7/3A2RGNhGZ+Tg4cvUo6NsagkORG2e+IZM/fu0NgvEqhBBi8bZ9Qea25Lq/PUSaK1OY8XRcCc13q/qkHv34TTs3iTeqSxuDHLsGjvtAgn9kpaAhmVE8g/4rbomaoA2uJsQGTuvjNJ3yX150sHl8uSeV8uN7mp68KhfLG58MJaVgfnmhbTironuPwpTlCFxQoMJ3fW9w2bgBfewY+fj367B8dGjFezanDiTp8cg5qqFoTrxliwZJ48BM35OvNJt+ovG/NUCWT/+uuy5fmwImBMgZ5bZC1fXlxBs9ti5Pnp6T3/FcjI4IN5J3S244TRpEpfon/5/F7hy8clH92t8iJKW/27apjrbznoD1zuKcJH7uWflTpYUh59iWomXznVTEP3/dseGFX8Br6BlfVtEh+a1Obj1nLi+giTx0QzltsbOKF/XaTJ6ZiLnfugdMyMvjgWIP4ZBS5bdv8nZy5NGTLmZbsP5ZyGqTM/X7QYt+Fv7UlKsmUTnnPUM6PNKzmzBFVfHfDdu0IsaACOjbGYG0tYwcpko3VHqsLIfj7oVesQjqD392Hx/1IwP9eAUQE0Wa5ox5vSfE7+6KK5eOMeJM6RaZhUBNnzRjWItgmbxylOYMgCDfmk80Wn7stDF0Qjs0MjcEFearyM9pumJhLjSvkVqyc/7ImNPtXn4FntWFbCt2HJ9o45AU3J2ezXMitCOfW+f2yLOsUCjqqzPojhR9LAd+ybf72NmGklRqf9PjWTBkZJIzBb0oMorPZ4HRDrYeW/O+Lzw3qMjL0HP72iZzfLo2BkQcTSQX2iGyJ/tVnZOcGU3rPnslDsmHgTI7vXt/mNb0cfpKy5nJtrRvO5Vt9MZf/153xiOztB3HDum3bvPZkdnlzKUwk96S93uKvJgYn6qKc2NTm8o+UYZUOk+KXRXqODkhDN9LQfbgmwvmvu1PM5Yal8j3wEbk3XTmXp1yHz8tZO7VPWjlOgxrfWxzBb+ceVXIr+L1bOSmWSVC3MAPoeh7/4HSJrjpCOjbGwPfLD/Y83mhxTxNeLb1GJQjKe/7ve8JT3/RcNnKUpSIFE52sxVtS/C9Z6fDJNQUTqWP0kdB8gqwLH9XSh4mS4yg9ZzB2wPUZ1OuMa7XI0AVRjuI9A9H36CFf/ObPaOb1z4DSokGpUN5hCIWijOa6hEmu2SaXZ2IcU+nN3uHYfNZWMbcymOh9tTYPtJSXKuYowOCPD2XSTXqXPcfhCwuiHuVhdjKEfOrBh64JBf3ASdlc0Pe1fI/JwxMRBX5QnmT+5JpNJwsiAgKpUMoig/fISpd/kHBSAuDtffHKjZp41uc0nRBrzhhd3htiX/Wlwmy0ebALL+qCmks/MHh0Igob1FxuOU6Bci7ZJ3JsV1pibF9ySmHkEeSmDHkg+Z2w7dy1U8q5RrYRV/mQPzdaYZm2fHrI7/f74pXrdfGsT284uXKu9klTRiIp4yNhonebbU43BFQKEUz0J4fR/NhSDj7etHlBt0RXHSEdG2MwHk/y31RYaavFV5zWkzRio6zXDC7M4Lsu1/CBluul+qQkSXkqJ2tR51LXh7fcEO+/DAUTxXMGGAbf+yxROveS7XQNcnIchkxEqSqMFMlNeaHVomPCF4XCJPidqBv4wI8+BY9JA/Zio0Jv9TxXXSqU99Xa3FEn9FKNhIIejyc8ZkVCkI1xl0AbjgdDG880uNpq8WV3SOOoVWb1XfjgSCR8QSiwz97KD8OVUmlnRAa/uw/rU6G4ez3xufF4EhpWdUr77X14hy3+eFW9YC6lQpn5OTDgYRTRvaaXhtECIgX2uFnFkMvvZRjW8XgCfVlz327zd8NILdqXAL/fh0eMKDJQ5wqSVHoeRXrOH5Se+pcrKE37bSoyeEQm9c+4Dh+TAUmJn6f3AwvSsI2cy0vNJq/ehDvDgDxy+MK5lOP7tEb+XIYJ5DzjI8d3qdXiK0+LSFSf9z8ZRLDUTdmy+gtbQicU6qojpGNjDIooOIi8lC+PwRVitR7sSqjCMBioS1ZLLqNQm/NELYKJ3nEgEqrP78CJWTwyiGAXg0/pwfBT4QvPFvepF3zUeYac36NtyteejJrpqbGdlpmqf/tR2G9I72pSNLYSWEp66k+1WnzpHTocE0FufyiF/M88we9lRr7QhTBRVmQgeV1utfANIyz1VZfFKO/0Dw7g525ajCyLehCw6RbNp5ggdReFLuRvuaHnenR4T/y2U7K99z99GD4qFdizCi4xf8maWLt928bJ8i00JyVexhwZ1t/vw8NjeEQasVfUCrrAFrUDd32CwQwPIQfKGETzLr779j782PUarmHQ9TwaJV5sbnwgnZSHG3GnIdonJu8eiNPUbxmJsd3n2bmRsGEYMYOQUs5aJPK52nmBIIjP5Z4DD8mo7uML5jKqXsqAiRwPRjauYXC92QzPCejw5b4bVZ49IZ2iT6+XG/KjpGNjDIp6hO/viY0y6bbiyTApCC9b03agarA2LjMG0nuyIphIQUR/5yQYo2TOIJ5ABjKPxifHYVpRvXMmacZAx+WVwjyjHUral94zkyKPqDj8D6SCfjLmXcYht3cewL7Z5B2uYP6ivN8uOMrvZZDk9Xirzf0tvcW2mkuxvX+/L1pH35DGTt1fnMktLAmOJwaDAH5jT68Ck5hxqxnyOyUN6zUH9iQvo8CwvkBi4SYOv72Xfj+QOYMrzSafGYPRBL9ezeCGA//ow9HadaaLzWXbEUVRV1otXrZpatUr8X35KzfgJ64Y0V7JnUtlxHPePozvS+U0qH25UTOY+aI1y38fiHk65RQrS728NGkM9Ln83Iw8QMcyuDgTnUlvyP5U2wX8FEykOg3H+ElI6nKrxYNrBi8NdYqvfigAf3gg2qI8LnNCD0qn6PZ9BkdMRbcHHUpjcO/pVszzUN76p20ZvLQrepN018sVCkRKZUt6F1dt+N8SIvriU2g98fN/V1Y3yuQ4Iqw0+/c4+9J7brf5tJ72hhTSU41owLVONUMnvp4h5X4QCvlsvZWo2FGfN3nPAP7rjWaIzXYKjE/47YLI4Ml2my89pZ/hEp9VcN/PX4NHJjBoybkblRuDhoTdlJD/5UgcUrrLiRvxZrMRKjD9tPa4UaYs46ed9fbIimYScnPWWzxfKy1Uc/GSbuSt74eGbrG1a4zFHrvYbvMFJ+KfBjANk3VLGLqRB5NWsRyotShLIF9qteLVT4l9+U2PwEfVPE3sAuuir13aGExVwnqtxctjUJNYuxd2hSp806XIkBc7DeIzlpS7WM5ARj2XWq14jiP86WJs7zyAH78UOSk1ye/2TWdHTEUXRhhSgb3wTPLggFi9rmXw3o+Hj3wC1LoVIwNZTdSTkcFv7Yv+Imcb8HHrRMZAwQ2JcByIohDNu0yOw9SO4dsZrYmv35D11ZutxCEyIQSWYfK1Moz9rLuqKJSCyGAwxfADrjabvGrbShyQEb/t/rZo8Pf9F01uNOXYC5RzUW080tBdTBo6LaG7reUi2qqV8LDcGCSrUt4qPff7PbVuYq4ODgahkDcMIzzs9eX3K8NTBDVEUd0HRuluooHcl89NOCmK4Uu0iHVro9z4hPmXjKmcXBIlZU+223yi3thO+7Bu3DtrxXJQVuWmnJQb7RafFHMapGGVkNtfjWFSq+HWTJF0s3NyY+inkNPGwJMVgy+6s5W5L1+sGdtat7rTYOJh4Mf5aYZOb2ERwpdybt66J1pMhBGrPMNy+3Kbp4m8ADZGYvFeejbbGICBZYgTi6qeuypMtCmNgdLTX6RK8RIXpCQTdUBkDAryE/e3LLzAwjQCuu/wotPEkgYy6jl1Mn9sb34BPPoJ8IX3liuU6OtZnrpMeLbb6cSi/PyLOtGW66goS0FmWWwS/9fJ0yKDT9AUii50n67BK2d65UKO1mAQoiqwt8r7JsKcwVo6Z4Bh8sPPgcc+Af7BsypAbupMg0xWPzHV3nR92hMbD3jFnUnYQOayNGPw8WcqKLD4r42RL5XlxXabV8W6nEZj+yyp2Ho12Nos5mcUOQ2AvSfWbutUO1FtpXJZ0fctA8wqCjp0jFz2dMPq+bTHU3zgE+6Kz2UYZa1H+zKc70KnwdCMedz49KUDdr3TCisD9bGZhknbFJHmwIPNzSpO0dFTSYHkfLSzc24beDPw2cAN4Nt2d8//bM5nd4B/BbSBXwL+ye7u+XytsCTVatn1fR/sezzoODiGwdntRHiW5a13KirMMIHssGaJS0ZAVvM4nsgDyNOOEE8gh5QRGSTHUTMRVSmBRx2b3+/X4nXtMmR91ul4KUqkEAxMQ5wFYA6YKFOlKOXcavEpm8lPCwX94JoB18RrX3hvBeVckEA+vDFhCwh67Yz7FsR3v/sBcTXpF52E7s1y4xN1mxXa5IoNQ1ckoc0goD2Jw3u1moUXVpYZGAY80AaCcqfBlA5DSx2UsuEl8j3/YIqJwPA/bTuhWOVc3NUUlWePTuCVqvvsoMiw5ucMDvoOG4Cz2Y71UNIN6z8+K072fkoPrCeUwszmV3jOwA9oHoq98ty74kdr1Tp3LYPP6MHb+uKgndltCI97bMN29nHcvJzBeH9KJxCe+t86mfR/Bb9ntQw+d1sk4z/9ZPHYIn4WQSAT1m5kZC5cm9AD2r12/CRxEDl8r9qIyoI//k4VGYi9laerjppWHRn8KGADp4GvBN64s3PuxckP7eyc+xzgXwOfCdwPPAv49yv+LTHa2FjPfP19l4UVH3SapBt/RF5RSJ20gs4ipVQMnPDcwnYNUU8/0hRKop2EkWUMNIWSNY66hj2/T4swbR/WhzLqSTaPCeIVN4Kf3JQVEsiZCuWGEPBrnXYCl42+8YmbBp+5Bc9pw99/oCG0xsTJ7cBVpMAc6V3ecUey5jJK1L2wC498AvzQcwjr46uF/y5NU3huv3ZTQFuf1nExXF/cMyFbd2xsrGcDIa16NLacMmTVB6kpE4eXNd3z2FOizPNyt8OzUyWlEQz2bfeJlhGNjWoKTD0hSVPZZ2jjRMYxcMGNtgXf+YC4pS28/CbH+BTWGRxOqUk48RXbScUXydwvvQT+xwvh/3mAudYuCRO976LYJ/vdNidTcLzy1g1+82UCCt7eKh5bkl8NN5Yz2L8pZO7sHXGZC4icBnX+5kQdXnVn3EnJ01VHTSszBjs757rAlwDfsbt7fri7e/6dwK8AX5Xx8a8B3ry7e/6h3d3z+8B3A1+7qt+SRfv7/czXH7skNoqzkRaCUOj0F0OYqFplQxB4/MCz4Zvvht99ufTkhxlQQ1HOYOqE72eNw9SUyqMa1PDevs/p6RTPgJMn8mGikHRDt0B7xKvyZjhrq50+4St/f80w+J2XwZ88r8/JloyMAvITkeGvTf+e9oFQmM+6M+kpRsnqGK1VUSjyHoLA47Scjp+SrYO/qBXPF4BcjyBSziGZRvS5PChF82RBRAaKHpXGwNnqZCjWDONSopwlR/n/9FwG8oKes6fzDev8/LLJ2xO8LqQgKUBTmL266Ni5ViOay4JIS4eJbkYiw4ekjBtbWf1O4vCsYRAfW1F7cWUMjKiU1fGjUvUX58DOBgZvOAv/9fnwKw9CYz2+T/J01VHTKmGi5wHe7u75h7XX3g+8JuOzLwbekvjc6Z2dcyd2d8/f1D+4s3PuDcAbAF73ui/iwQcfBKDdblGrWQwG4jx+vV5jba3L/r4oYjcM2NrqcXg4wHU9Dg4O2dhYZzazmcqOoe12mxvy1ihro85wOKLb7YTPcF2xwsPRhJnbB6DXsjCB6f6I8V6fbreNYRgM5SUZzWaDdruFLRNdk/GAl56Cf3/HIZ7js7cHvYGAAJyGyWCvz9paB1dWqfiB6CDZajU5GAzYqpnCG3U8+uMR/f4hQSBaDo9GY2zbIQhUFYXNYwOHvb0RrVaT9z7h8gnAjW6LxmjExsY6+/t9ggBGIzFvjuOytyfGtr7epdauYUxc9i/doHVinXq9xuHhMJxjpVBc12V/v8/WVo/BYIjjuAyuis91txrhM9U6ObJMbzKZ0d0IODgQx0s32zWsscPw6j62LYRnczNaJyWKjuOEz2w06nR8i/WZw6BW49nrNtDi4OAQz/PxPXV0lfA73W4bq2lSA2Z7Q9zRmHa7Rb8vPmtZJpubGwyld+Z5NmcaARdmRpg8/jRDzJnbrnG416fZbOC6PlM5l6qJYL9/gO8HbLRr1IY242t9pvK2vLW1Lr7vMx5PwisXVSXYYwczDg9tNjbWuXZZhHiNzTpBEDAcjnAc4b0HsrZ/MBhje33a7Rb1poUFGCObQf+Q9d5GuNZKFtRcHg5GuJsujuMymQjFtSEjyDvXPfb2+po8iflTOlHJU8PwWAPc/phDOcedThvTNBkORwwGI9YRMJFaA9M06PU2+cjj+zwfuLHW5mMtj9Foxkyeu3EdMYezmVjvUJ6sgBYwuXFAm7vCtQbo9TaYTKZ4sh37mmlju3DxRp81C65cFbLZ3bDY2+uHa93vH2Cr8tEAhsORWMMgYKtuYjgee1duQrNGq9Wk2WyEyd1azQodhzouN11wvID/c3XCfXJOT2zVmEym4Rw3G244l/v7fb58vcb6+hr9/Sk9wJd6YTgch/O9sbEWW6d59R4IebKscuhplcZgDThIvHYAZMU8yc+qf68DMWOwu3v+TcCb5J8pM7293Sv8Ww+5LMui02nT6QgP4YkpdGTf8RN3rmPIniDqGQdXReHY2toa3XX53DWxCC0PWhqv7US+odXqMj6EliwPjd3B/FFxyW99qxvyUl0QTdOk2+1Ev6NdFx7K2KHX28T3g/A7qofJzaeaeK6Aia749fD9i1cEOO9vd8N52NoS7wVOm0OgXm/E52ytCROXrXoL2q3UnCr/sFazwmetrwtM6Iqsfrnn7vXUOtTqNewpdDodDMNgc3NDfGa9DTcnrBl10L4TrZPwtq2aFXvm1b+6wWngkfU1Pv7MRmyOXQeuAoZpxn/HppivpgtNfY412tjocQUwDJ9ntw3eLWG30w14icwj1LR1m81sut02hzegIatCej2JC2524PqYjm/RSfBptZr4foerHwVT9ibaN5tsbDRFsUFfGKX7793EMIxwjgEOZc5lfWOdtQ3tuWsNGNqsy0hRrU9EUlmudajVatRqNdrtFpdGPmemU5Gsfu5JTC2s621tcPVxkfQU8yPl6Yz4fbWJmymDGwwIrkV/69TvyxYXWx0sy6Lb7YR7fu+aFc6P/r3W9jpwmbYvfkfyTvNut0O93sKZyRYwLgTrPfZ8aA/E5dP33L2BqT2z19ukf9XCnoioLtYTaL0Fe2O2623Yjl7Xf9OVMC/occWBvmfw9kmHb5sKOaifWKfeFsYMwHPFZjLM+F7u3SVOFJojEZF3u53Y+2qdknNc9PciUNMqcwZDIBn0bQBZdVLJz6p/H1lNVdblNu/swz0TFUJmJaUyQsQKVQ0Q4YlBkHHaNTxjkFGRksdPwihZ41BVDXXD5qKMbF0fBjLq6Z7KanyloI1E+N8tw56zoQbXh5PSW3nBPRn8gij8j41DQTc5/PKqiS48IaKQ/RPrGT3eMyAwiOayAFfX1+2lWt7ja86Apb63Ea2bGEcJLJWXZA1bWLsY+GGnzfcN4U55b8WJ0+l9mVlsABWgm+z8ywcujjGBa2udmCEQH87Im1XgVdSOYiTvHe6eLJK5xNjUfdqHRWsnD4vVohYRb92Du6WMm1kyngXPQiUYLGo5E5Uh/95Vl57j4NXM2N3HoMPOCV51S+xNP4DhrPwiriOiVRqDh4Hazs6552qvvQx4KOOzD8n39M9dTUJEq6ThcJR67Q8P4R55zR4ZeGKm0IUCXmYMFPacYQwStyDl8gJNgQl+WeNQZw02TYeRJ67Ye98QzgyVMZhD6CqOLyniH9xzuWNmMzNNtlP5CQik96sEKByHvNs3vAehIr+BhFHM02kPKMjC1KGaIZe/j8AN6+3XLPjHZ8m8b3k4HIUtDVK4+lqxYRXliVHeQF2s/rZ9uE/ty+0snDter16VX/j1xGxeeEqsxaSXZXhUrXJSWbYKeRWdVO/cEIb89F3pRkPhXCZVU7hP8vv2qJzBthk3Bsrho5exLylZu0H+vky2nPnzIezJ5LG52Uo/M1BXzWao3Z5c5/4kU8ZvBa3MGOzunh8Bvwx8187Oue7OzrlXA68F/kfGx/9f4Ot2ds69aGfn3Bbwb4H/vqrfkqSbV36eK49/K4f7fxB7/d2H2kbJMga+UmCaha8ocGiJyBSF96OmE8hlAq5w4xgr+fvuaojfe3EmWjA8ILFsTmQIuTJSRgJLLFNgid+r6MNPCl431zuZ1zF5nnjfrHXj41Dzvp8n5Nmlpc1rwhhsZykUT3qCVmLcegI5JzGoG/FP7cGvvxT+6GNlueggHRk4jltBOVdreHZJRnVv3wu4V+3LjDJK3x/L8SUisPVi7znv0NnhVYk/Z0SQvlw3y0rMsx71ZBQb5JWWDlx4YF/kaR54IJU91vglfstmudOg5nJLKucnZ/B7+1FkQEZkEPhSeZsJ+V+vIOdyr5yoCRn/6Stwl4SIjM204VFjS60bxMaXJeO3glZdWvoNiHMD14CfQ5wdeGhn59y9Ozvnhjs75+4F2N09/1bgB4DfA56Q/33nin9LSPbsSezpB3j44Ap/JnOLU0/Un99bsFF8T3gwVk0TBB22Kai4KYaJMoxB1qEzqFYBIzHiu+ry4NBMNMW7P/Qus4Rcjs1KeNYl/IIcj+/mBaGcJ6eyscpcfppHVMwvmmvHhzv74nn335fm54W8EuNu1ERZqOvnlgbr62YY8LdPwEvU8mdEBgCer4S8QGHmkFq7EzUHOxDtHh6+OqXp+3jdRnRBu87PVeNL8CtTYBlrZ/tQl576iTPpfaJ4pcZmSRgkIHOv5AUG77884/RsxqhWo3Uy7YCptUspzDmMgeoU/AvXYOb63DmbCuu0kVbQpXM5KC9lVcbnN/fgrNInGdWJufsSSuXgVtBKD53t7p7fA16X8foFRNJYf+2HgR9eJf88MgyxsP+/p2z+60fhxifDR8ZQc1zReMwyUpfMgL542k+3TJHUnTjCIGR8T/BUcENGZKA8TO27vie9PTNhlLpx73J9Pb2RVM5A3ar2xFQ0+HqjMgYZkUEkBInnleQMcoP/K8LKNu9Ke3uQFvJwHKVCkI4MHup7vHQ0wjUMNu9MRwZhFJIUcMXv2lCclla16zq3IiOeERmsr3e5OcyZywpRpFJgdzdsnnDhV2/AqYGscMtYN9DmspY05NXKPXWY6P1DeKGs7OrcneWpCyMfc4gUrbdEqedgmnBsMqIkSRcfF7yunljnWRkWI3LAEmNTivxwKpywjOgzCdu8dQ/unk6xAsSaZdxo5IXjy5nLwrUTkcFWLZJxFRmQERkU7kvljN4csb5+Ty7Po6Rj0Y5iIk+DWvISkXf24U8GGi7bS0MbQRCEG2Uhj6+KUtEEyMuKQnResuRRlYvFeEmsVBmD37wJ45nH2emUwDQihauRlyd0pTmDtKc+cOH0TSHkd9yb9tR93ybwZ2BYYTgejkPBINeHOfwEHboRvPGRC0OR8NzoZgq4L6s2UlEIRLBUjvERAm4AQRriy1g31/XylbP63GGBNyvX7i65dj9zVc8XZMF7vhZlJd5fL9uXaQX6x4fw4KEMl8+mjYGXF9FBcZLVUP+LR8/jpwQv50yx05Dy1BNJ1ixSc7llRXs3hIiy8iFBEM5lGnKrnkA+VYtk/OMCudYZ+YnCfXlGvnZ5kCnjt4KOhTH4hRvxk54PjUS+4PnSo+OODNzZn0LgYZjN8FBXSJWw4BxjEASahxltmNxNmYgMVL2xTqos9ZS8Pe1Xb0YKxdjKxvD90NDN612mK1L+fAgvlgqlkaFQIhx4PUwshuM40RWCrloNJCiQl/d80yMe//5x8drNJ8VcTU5l33SSa1ihUjieu3YZMNFkMtWUc2LtlLeXmw+J1u6svEjl7f1iYyAiyADT7ETRp6L1cigF4pHBh67OODObMatbmfwimCgD2ihUmNlVZ51rYp+sZUQhAH4eLAXR2t0cZ343GRkAPN8uSB77MwkHNjDNRISvPPu9bF6Codibz2tHyvuVMgcxN0x0p5TDK4eZMn4r6FgYg8+TvUZUz/+/GpcbgzB8zNqUVUJIM0ehzFzRm6huCfw65FeC4RfyEh7RCSvCwe8vgIggqnlOjS9M6GYLgSmV+Z8d+vz9h4Rte99Nj2ePRniGMf9cmgaoiqAr6critVrUDEwZA++q+FzzTHZ+IoKkChRKzvggxxjMXNExs26Gl5gn+aXGp+PcOfkltXZ3aArs/gJjkAtrgDCsADeyo6zooqDotcmT4ojP5I61TKA/N9cD0VxmKMwsmOjQhXv2hDE4fV9eZFAwvjORwswiBbnd24zk4Aus/JxgIS+1jwsiVrVPXtpx+bJT8HfvgAeU8cmEiVQEmbEvT66JKHd/EmtZfyvpWBiDu2Tv9a84JYzBHx6I/vYvGkrlk6XA3AIhUAq6CJvN8y51qEETvtxEnTI8MkmXPHwC6fpqgAcKFAoUeM+hNzvOrLh5tsTZ69j83DUxj1eeGmABh9vSy0/yctNwW2wcyiu6nBbye2X7j4aM6vYc2Lopnnfq7rxkdUE4fke+4QkpXDstXNejAm3d2u1W/trVLYFV+0FuSaRSKKfr0T550axcgWUaupNd4ZDfHGf2Q1K/+vf2A27Y4l6CnvTU2zmeemGUVaAws6qJ3j8IeMlAnuDNiCBj/LLGp6CljH0CejsKh395r7hL/HOM/MigcC43WuI+kZGd29gw7GNluPzCS+B/vpiwHX6WMfCLxqY5RZ3DKo0iV0/HwhgomKcnscTH5Hp9zFhu4oxa9XCjZHkNvfLwP/ecQQbuDAUbJREZiJYQSV5qfNGmfdksv6wUdOOTGF+7LqpEHD/T2CnlrCC3PzkE55IUzhwcOCspGBuH+l6Ggq5bQsAfkDDKL12H50uF0rwzJzLIqxABzbssuN9Cldvqa5ezbvV6LT/pCZG3fi3bw1SG/JRmyJ8/lWuXBRO5BbwalvDW/QBupmvVlVr+qSvw+X8BfzGEBw9EZFC7u5f5+wqjLAXTXc0yBunI4JErU3qOIy4ZSsyjokKY6M7itYv6gbn8h2fDH70C1ofKGKRhG7/I4TOMaHy5a5dw+IIgguiKIoOssUG4N+s3np6KomNhDFT5XoMZJ7UjA88e5MNEhVZcKdi9/MMhudVEOUol1wPTD50FQdgnKMZLdS01HOSdILzSyS8rBb1KpECBZSgUZVjvlWca/nwAJ26IZ63neZcZibPYOAoiA1UJdm9TzNvPXQkieO90cc5AnWmI0el14bZeH4oS0wwKlYqvN6nPDv8PD4fFSdY7S7xZuU/uaUYK5aRSYJkwUcG+hCjyKVHQ7x7Auw6i5PFoI7uwsDDpqeTm2iAVRargyQ/gLdfF//cvCF6HpzZya09zCxugPDIIjUHGuhUWUeTMpdpfucYncZZo4ggosVkTXWsTVGh8INwrzoUjO3tbSMfCGKgknR/YvEDK1/ZsRndqi4XLsuJ5mDpExiAnkQUFScjMMwZ6AjnBT9XGe37sLuQ4r0h57T4HPmUTHpzl5wyCwJVN0sz0YRuIlFAWFiyNwXNkB89fvB4lj0tD/zyhUwozAwsOz1BI4/ORyxO6nse428wsDYUSnLthCWPnB7l4cOba9bOhm8KqM31sl7IVmIKknt1y+bZ74YfP2FiOJyK0dlqhFPKCQsPaC/W9UNw/dzkI1867I9tpKFy7zZbIn4ydVBQZgYUBr/tL+OGLYF4WUYiZs098f0YQ2BhGLYx2Y3SyK3I2OcUG4brpRny/yBgUGDooNeQpKLggKhD8ChLIGj/r2l/zE8jPZDJkpUDg2zxX7onQuzyVnTgrTC5t53vOIc/cipScyKAI2tAqfLJgIuWtB4HNN94N7/hYaOznw0SedsrTyDoaf1JBG+nxKSFdN8U4npxFxoA5ErqxcWx1hJI+nKUOMKmxna2L118gISIv53Cbzi/Xey6BijLXLkep1GqurDprhW1BYnS2mjcLDt/7bNjpFud6Sr3LAu/5jLw9TBmF4dUhbd9nttmmllH9AgWHwMSPz1WYZ6RnXJPN/d74FJy5JozB1n3ZvXd8rQ4/s52FaRTmfFSEHEYGni9yPQawmWEMiiAp0MZWvE9COFHlCzIOt0HB+RBFcl9a1wsqmI6QjokxkMrSt/lceQvY57sVoYYsodtsicNnQ7vAW8/LGeScYlUKOrMcUn52fxLrXhnxSsAaM1f8tpqZ4gNaQrdkU3I1rVCUcu7InEHHdXnWeIxnZlcSgQ41RO/HxmEauZUiamynZdnsC6QR75zNEWA0Ic+LRErhhoxWIqr6KNG2pNMWXna+4dHyIVltG1TVmS/3yd6CiX9FBbi6MvxfcYcY18tkvsC6ayNzX0EJTAS5xsAy44n/x6bw8n4fgNa92cYgC07M5ZcVRSaN+OFUBEHr2QfOMjsM6FRqyHMig4woJMYvb6+c6ELDwhzMKl5StFo6FsYghIn8GV92SvSa+efN/EoiyFZg2gMjj/tGTnSQVZEC2RfboNf9FyTqrg8zL74wtMgAiCuUgjMG5d5llkJR1UQOWzV4kfTU7VPrwkBmUFaUlRqHEvKnEsZAju2UjAxeKPlZOVEIVBnfApFBCBPFBX1/75LklSPg603x38zNLGdN8So1BiUwkVq7q2njo+by2U0xly/Tksd5F6p4eb2JFN2ZnfxXvDbMyGl4/nCIa2rRRIpXiXKG2OGsJKXmsiBfIPiV7JMwYp3mtNxIOA0H2XmlkF/eqX9FpgFnpaF8KnkbwNHTsTAGurI0DPiETWjfVMnjYmgjP7lUTaFQsZqoENo4GRmDrP5qynv2VWRQplCKICmIBO7aIFWiqM/lXU14yWFxGwrIPkORGocSgktxIVDGZ8O0OVWPKony1i3OrwwmqogFgwYTxec0hDbyoiyIPMyMvEEq6blftnYlCqxdFwbL9VM5EXWw6n6ZjFfGgLs2cy/0CnNZuVGW/B2XsiM6ApuPXQt48PAQE9jbXs8sP47xyls3mC8y6Bd76qUwkWlExjVz7RLR/0ExTFRpfHdLOXjytjE4EtJhopBUtUVOZFAqdKUHYCqcM6jKT3WTvD7KLMJIYaU3q0ENuQLerInvekEq8lG8fN/mM7YiY2DmeHuQXS6YGsdd2R5ReDI0sPm1l8LHq5LZzDsahJcmFLSRjXODWDsDUTKYVY+fNOSOJ9bNNFJeXxCUYPhQmIjMjwxKoIYi45PDL+phZfNAC16mcj13bWTuq8DXCw2y91KMl2ZRDMMMDcLLu3aUqL4zv1d/JZiokmGVc6kMaxlsUxSJhFBRhnJORv8FCeQg8KtFPqEx6Od/5ojoWBgDvZoIEArgRrEx8EuTPfm18VBgDELvIaeaqOg05JXDjNurovGFxm4vP3kseFUQujAcjwudKUs9g2DG+WfDV1vF8wjZMFFqHMoYXDqMwRu6IX9ly2N9MBFKOS/BGiYhO9nJcRAVWtsdwSejhjx1elwX8gTs1m6V5AxgMWOQedlShahH55dQmJFhnfHnL/dE+3bTgBPdzH2l88q9n2CjJc7CzNzUuRu1dg92bM7KBm4nk3csZ/ErUpZbHeGsDGapCqZUNVFlmKiAX46TEuNXoZpI7MucNiI6qfMet2Gio6Gomkhunptj4fVutcXGyqDSjVIaGWQkISeOEJq6FSuLjDwwK7vU88yG6Kx6fcTgej+DV15kkFMuWJQPUZRTSREpZ4eGCafkfdJ5OClkz+VgkFDC3YYQWseLwRshBBbYUfJdJfAzeZVg3IoKSj5TQl7gYY5Hoia8mndZ4M0qBaYUagnElxvV6fySkJtmWDf1w1iWmV4PKkSQIb9shakikb93yuE1hrwKMuMkcIpf0drpOYcUpJiAbSrDRItGIupwopTxgmqiwvMTOt25IZpLXh2AnV2cclR0TIxBwnOWvW2yTh4rKmxHob5rANdHmYeXSpOQeisKzXPO9MBqJsjwOrjYT/MKcfwVwUSQX92TnMuccxMxfhkwUeYFHirvoCkVU+enKrdyDDjoCdYSoVMKrIq3vp+dPAZwnMNyfnpSN7FXYrwmjvhPdejMoEpRXc7YlFPk+3bKi81aD7+K5wy5xk6t3XbN5m+Zqs9/vjEoLNrQ6a5sBR3BpYkEcsa6QUl7bkX6XCYT8vralZ0+divuy7qFd7IjqqDyzjccER0LY5CCiRQOntP1EirUBOuHl7KOqxcZg4SnUlrVAHBvT3wms4pCeZdyfGVJyKJDWYpyEnWpuczJgSjS2wSXekV39cT/NY9PnUAOfFuc7oTMC18URe0aqkYGWeF/IqrLOXAmfleFsTVrArLz0gfdIkjKiecLyk7oFinM0+vCg74xipU+h3BiUNWwVoyyzmoQn0aR4zCLmq/lHBTU+RVGBhA6Rqm1S8E2ZTBRBTlQEavtpRoAxozB2Bbwc7uefSFR0bml5GfPyPHf4iTysTAGocfg2+KSlEFx1r+wZ7xOBVBRZjVRzsGlELYpUmD39ABoZ/QtCZO6gSOMkzqduZbjXVZJ1KmoKZFkNZKeuurkmaNUgsAmCJxUm+CNjYyxhvhsNJ+xstnQGGRXo0BFZQnFNeR5kUGGUrFkP6hFYalY0rOfXbGkqFJyHEQkeXpdeJdXMuZSN6xy3bLWw89oMJhJebBUmF+yI8ijmb92lRK6EEUGqQS5qmByZXPAfDkPgqC8mkiRGl+i7Dm2Tw7Sbel1qhz1ALV7t8U/bkcGqyehmC0gEAtX4s1WTvYUJJHnqVWPSs4KlLNKLGXBRHoCeeIIJdCuF+DqFYQup6IoUl42wVTCUq16viebIwSZMFGGcVVQgx+DiYqMQUUFdseayMPcHMPUib0VrV2i3DMDbnCrCnkedKPvk1GxEfe0O3Rzk+Mhv7QCM8K5nKWUc9Z6VPZm9eosJ8qRxdeuPKqrFCFDPCGvFxvoOYOBvJt5rZFZyipydAUnx3XKLXvW+JWdMZgjMnBVpVxeC5MjomNhDEBTmIFdagwqJ3sKDi8VGoO8yKBIoZzdANPAuDZKnXo2daxUKZTCcLyiR5QxPr1kMJhJz6vAU8+rU8+8wENX0HKMuvGZCyYqG5tlRtFPMkmejOoKTpa6tswZ1PJLa4GCpKdmeFREl5cvmMO7zIL5YlVnag/Jucxaj0qlnuoZJ9OQaQy+VManKKqrOr5OQxhmx49BNynYBgpyLxX3CUSnuq/m7RMv88IqnSrPJTDuSeOUKNc9ajp+xsC3c/sDKaqchNQPZ6X4ZVQTleUMioxP3YI7N0TfyZyqjSCwNSHI93YqwUSQa+xCuEHd91rk7c2jwCwzKlGVgheHNiooFE8oP6tMOUOBt55IRBZUifj+crBUpDBd0fANcg15ZScFsg25qqoLZpGnXiEZX4nf6bTCzIb4KiT/q/DLinz0qrqJnMt2zlxWdRog94BppvHpZMtdZQcMCDaaIrIfabrqFtDxMQaWqqSYlVbA+FUwfNBa+A7TfWey2lGU5QzKlHNZFYWveZeFkUFFIQ+FIEeB2XIeC5Wz4hVXzlmX9ACpvkHqXINfMYE8jweW1+EzZsj1O3ez9kswlvzmgKW0yC52pmGFCiVLOcdgm4RhzVoPz53DsJ4p4VcF4puH351pQz6fcp7D8Ki5vJ7InyUrwSCz06zOr4phbXfahQUOR0XHxhjonT3Dm4vWSmCiMqFr1YVid/1UB9PMC1JycgaVN2Yudql5RKPi8Fjwqzi+nJyISgQHsyrGIFuB1Wo530n0uol7l3NADZW8y7z51IR8OBM5mG4jMwejIoPStYvBUlkKzCmNDCpHdCBgG8sQDkgIuWnnbRIwUdZ6zGVYsyIDxc+bCRkxyG1FobcCr8TvrrSyjBuDYuU8F0yUmz+rbgyitSs3dLWaVdok7yjo2BiDIJAL584E1miQq1RKzxjodFqLDjRK5QxmrjxwZqY2TPXIIEd56VCKEoI8rNS3hTIwLAwzv+YbiMNgsUSdfHYFY5AndINBToO/hLeuV4LNAzVUMgZ6hU+slYK2diEWnHYcRNVZ1A68Mr/LWdBGOc5dKfGvqABy83XYRsJEWesxl/ecCUvJfTmT46pbuYUGvj9BtAJvho5bId2ZLmeNTiBryjkPcpsHJoLM4obFIoNyfoPBqPwuhSOgY2MMlALzFc5dsDEjIVgRngha0rqV4lu5pO6sVnoZU15aaWlJAjlSXjkH3HRq10V1hBOPfFJCvqqcAaRgonDd5oUaKnhgbLXF5SwjO97aQF87/e7jBIVVZ1Y3igSLKOM0a2Y10SpgIkh566Z2r0cUGVSZywrGQDUO1CDTMBqfynEV5SfmWTcQTpglz1LYKvLJiAxWAROBZgw0py/LGGTccAZzRllQePL5qOjYGIPQ21AVMAUbs9IpT0UZ4TEomMhAlLN62j0GaQ+zcni83sRXfWC0W8iiKhEnKpNs5bTZCIVuTuUcC/9VNZHkVSFnkBS6rEt6AAlvmALemDqRAjuKJGTO5SyZkUHmus3pXRbycsqhhnn2JaT2ZtE5g6z1yMv3ZJJyHDTINIwg7fJ9UngNaxZZZnQJ03XJT6+qm9jR78qgSn2JdMrIn8WqzsJDdSWGvML46vVaHC7NuAfjKOjYGINaXeD0ga282SKPaB6YKNsYQEKpFFQwzYNzG1mNs0LD4xGEuHqOMZjXu8zoHx8a1lDIizy+bIW5tpYDq1hm7O7ZTAVWqTxxcQ8sJuSFxmBeXhlJzxAGc6Ma/by1mzvKiket+r0eyfxL1nrMlaOADOMjFeOsyj6ZkxekL6yfJ2ewKEwUc4q0IpFxcWQwT1nw2lpXRPbrTbEnDjPKsI+Ajo0xcF0BiYQha5VTrJVgIg2XTV0KXtXDrFjKCky3JVwRq/03otOXJV5Y5dYQihTurDePC6OsKh5fNr/9/YIqCRWNXBvGy2ZLEsii9cWcHmZGa4OwmsjXPcw07Ba25q7KSzVG1Dtu6oZHGbucJOtc+xJSEGYsGZ9oR5Fcj8otl3VKKMxw7dQ+WVUlkaLE3owVbSQinxS/OTx1IG7opKce65I6zTc+seR4hfGFa6GelTgUeVR0bIxBlPSsjnNXUc5saJeCDxO3IemeQ851l6CXspbz80/KVgWJsw1G0lvPUyjzemCnMoyBrBLBKVbOsEA4DtEJ3IkTM6hlUY/vTwgCN9X6opCUMc+sEvEKsee5x6bDUtL4xCrBnOLIp1KXTZ3ukCeDb4hmivpd4GXKUhjxCqfwdUoYH7UGRknEA3Mm/hWdiq9d5lzmGtY55aDTiPJnEqKtmkCOWrLUIz1UhRTUm3O17qqp4ioX087OuW3gzcBnAzeAb9vdPf+zOZ/9GuCbgOcCh8DPAt++u3v+SEecUpZV8MsqQm4YQgie2Bdeg+b5xzyVnKoU4YFVr0jxT0ljcHUYez2q/XfFwbQ8hRJGIRWqXyB2sY6iKP8yz6nSuEdUmLtWQjB1ZdTTSHizOVGPW937Ckm1D9BC8UwhzzIG80INILzZx/c0haJFIaWRwZzec8MSbcxvjuD6EHNLqyZKrF1yPbxF5jL0nqWnHu6TI4D3IHIaZOI95jg4npCDvFvV5oXcQIzvYCocsZPd+D4p2Jt6Ir60aANtLRTkNL01xmBVkcGPAjZwGvhK4I07O+denPPZDvAtwEngVcBnAt+6ot+RS62WWPSgEs49p9CdSnuXkNErBVKRge+NAb+yB7b+7DvFPxKwVITjlygUKQS1Wv6NUzHa6ggc/3CaahFRhnELfmou40KedZlKSM14eKw82rIcxULe5aY883Gg4bKZVSlpj27uihSIjI/kF86lam1dMzPvrYYFoRSVZN0fx7uIJpLxyfVYaC5PJ2EbtSfl2ApPOy8wto7cF6ExMBE9yIii1npOf65F5lKdQB8mjI/vauuXYQzm3CfhWoRO0a2BiZaODHZ2znWBLwFesrt7fgi8c2fn3K8AXwX86+Tnd3fPv1H786mdnXM/A3z6sr+jjBw1nzMXqFVqdlZ5o6gbxVLGIKtEMefu44ob5dCfsdGpCyV1OAuVS6Sgi731uYXANIRCuToQQn53T4uy/EJekC/kh4cDNjZyxpwQAtNs4HuUepgLeZetmnie7Ql+rXr2SdaM8N+v0gI5SXnGwJFzmWPEYUEFpnnPhtkDsst0k+uxmKcu9/bYBj/QHJQK+2QeaFZRNx4ZgHDAgsCrDhPNM5dJfqZ8tiudshxDPm+yOlwL5fQow33EtAqY6HmAt7t7/mHttfcDr6n4/U8FHsp7c2fn3BuANwC87nVfxIMPPgiI4/O1mhUelqnXa6ytdcPki2EIC3t4OMB1PaYzsSG9sQ3UmPkewXSGaZoMh+IZjUadTqeN64hnDIcBW9twcHCIJ4+hb26uM53OmMncQ7fbxuq1qAGzS/u4ozHtdot+/xDfF15JEHh4BxMsoO/bbPg+k8mU2czGmT0lfq/ZZW+vD0Cz2aDVanJwoATSZHNzg37/gP39A9rbbepjh8nj15ncI4VHKjB/4mACE9+l4XnhM2o1i42NdcbjGwBMZzWCIGA4HIUdK9fXu7iuFzYta7db1Os1/F6TxtUBkws3ad/dCx10b+IANSaey0T+9o2NNRzHZTKZEgSurMU36R+4GEY/XKebN/dxXS+1TgC9uokJzA4njPb6gDxHMXWwgJFj0wmCcK1N06DX22QwuCp+l9/C87zUOhmGwXA4DudYrdNmq4Zli/zAwWzCWBqAwHfwRzNMoO9M6diiBfpoJE6ST6f7AMxmFnt7/dg6+TLJuLW1yWg0xpaTtt6pUwfsGwOGe31MXDk2MXa/Jsa+v98PA7+trU0Gg2FoDHy/xWQyTa3T4eEwlIX19TX29/u0rYAWwMhmNBK/wfeiyGBvOISxwWw2o91uhfJkT67JuWyzt9fPXKfNzXVmM5upvO2u02nTbNUwpi77l24wMwJtn8As8GiSLU+TyZ6Y86CNbduZ66TLwsHBIf5szBYQTGzGozGzmU0gIwMFlx6Mx9RGzZQ8qbk8HASYo35qndbWuvi+z3gs1rrVatJq1YRs3TzEORzQqMt9KQ1rUDcZDoYpeer3r4iFNLq4rpu5TkEQ6ayDAzHHHd+lBdjDKUMpX/PqPTXHllV+DmYVxmANSJaGHAClJn5n59zrgY8Dvj7vM7u7598EvEn+mSq43d7uFf6tvJ3B3hrDCVgqYt3oQKuZ+o7njQEXw2yytX0HAJubce+h2+3Q7Wo952X1S3Pg0JSvb2/3uPlUA88VHqYlcb/eXSfANMNnjA5FkqVW3ywdS6+3ie8H1O/qwZOHtA8d2vIz+5fFWEw5vvZ6Bywr9YyaJRTIxuYZDMNgfT3urdTr9XSfmjs34cM3aE/E5mq31hkfQM0TXlB7sxv+DoBarUa73cJ19rmCiHpOnNiOPXJzcyP222JRQlts9KZh0pTziAOm9MC6W+tgGKmxNRtijlvtbSzLSq8TsL3dSPzdE5VCh6JFw+adGwTOBgfXATxMtW5nTkBDfLfZFHM93he/c33jDD3tt/R6cQguVra5L57XsH22t3vMplLJScfBbGbDNt2uKX6P2abZFM9LrlNyPra2erAt53Vks7GxBYDhSDGqW2yfFK/t7fWp1+vhM/auCaXYam/nrxPCAHQ6WnuVbhOmLluNNoe1dW4CNV/sk+aGWIssebLkTWjN9jaNRiN7nTTa3NyAmoyKHT9c6ysfrRMEEwwJ22ye2ARNJkFERuqa2RMn7gxx/Kzy2lZLi+RlZNDGor2xzmzaB6K1M+pWpjzN2j77QKOxSa1Wy16nxHxsb/dAzlcjSO/1qnpvHio1Bjs7595Ovpf/LuCfAclYawNIF97Hn/s64PuBv7W7e/5G2e9YllrtdYb7lNaqR8meipg6RLhsDkyEJw8UGaRKFOc9uLS5uR6/eCbkJbwUIwzJyw6dzQM1KGxWYfgK2pBKpXQu07w2Nws2az0eHptGIh9SkhyfK/SHCDKUz481j5tIOCUDJloIV1e4eTL/4kYKOosWSuiCVp7ohu3HTVe+ps1jcj0WGhuIbrk3gYmNsSnWzVAozqrPGTTi6wZgmjXRS66gMqv0mtk8Umsjn63k2whhopKijYpzGa5FI87vqKnUGOzunv+0ovdlzqC2s3Puubu75x+RL7+MYujnc4GfAD5/d/f8B6r/3MXJ99TEVlSW8wjdekskqkYSi5UCH2LPY1vENJ16ClOcq7EaMJvZdPRuqZIiBa2w57zE2YHkN0+iTipjCZ+onIFRZgwKFMpsZsc9Sp1SyrkBvonhI+avlje2BRQKRHshVNBy3TxXdKk0yJzPaC7ncBxCY5BQKCqnlWtYJa95x5ZUYGYD063Ffwvp9ZirFYVOGs6tEshlY4MFTiCD2AcGYo08XxQ6qAo+Nz8Hs5CMQ8r4hM6eKvZZUTl3uBaN+F45alq6mmh39/wI+GXgu3Z2znV3ds69Gngt8D+yPr+zc+4zgJ8BvmR39/y7l+VflVxPhnJ2mQITG6U2z0Yxjch7HkY9bsLS0oK20vOeCJ5OZ1EFzCCqgAm951BBl1XczGMMpKGRibOIl3w/p7VEkdApnDmTlFCpnjNmM+7NlvSUmtubTXnr8m89qZvBcyGlongl+ukY8lBkvkJZlQJrRHOpFVEk12PhSETjFzkN6r0KVWfzGB/DSBk7gsTaZXZjXSA6hpSnnjYGeTcLzlfGGq7FMy0yqEjfAPw34BoiSPwnu7vnHwLY2Tl3L/BB4EW7u+cvAN8BbAK/sbNzTn3/D3Z3z3/ein5LJkVeSomyXMRzBqHo9yeiyueE4il5FLSVXkgI1jKqKMx65D0biCZeCRL3vi6gVLp5kYF8P6cya2Gha8ZhojxoY3X8EsYnNAbF0I27jDGYxmGi0sggrMqaIwqBlLI0zSamMjxFN8YtaljrkTFQ1USGLfnl3pPtz11VF+Nne0L5txCRQaBBN5kR3YKGtZ7cl/KMRqkhlzqlPufaqb3w1+nQ2e7u+T3gdTnvXUAkmdXfR15GmkXNlsT1q+LccxsDqazGepmbnN6CPikRv2obpdNpQyA3eKykroGpoLBGLdOTDfypOAlpNquf0AUNJnJCXrCcN5sLEUHKmzVNzZstvPJyQQWWDMdD6CZ/r4jL6QeAMd/BJV05+4HmXd6iyMBsYLqJ90ivx8KQmzY+dVLdcMwUP50q3zmeRQ0LRkhD3sSyGviefEbNzI7oFjnTAOkoJIzqEu8n+UljUKv1KrEJ10Lty1sUGRybdhSWpSoP5AsLJD0LqZP21sO+M8qyZ3QSjTZKNWNgmqZ4jmlIj0gJeR1DKcwcTN0NcecFk5CTqO4fwHBKFFiB0JlmwdZrJD31RrXIYNEEcoqf8vjk+1m4swbvVWpfrciMQxsqWW24Zi4vWAbDj4/NjMFE0X5Mrke0dgvyc3SYyEzxi/HKOZg4Fz8NxzeVMShNxi86l1HECmB4Sg7K8nRzyLjO7xZFBsfGGEynAkOsbAzmVSjduPcMOtyQf1J3XlhqOBwJbyd1FL+O6csxraqdgaIkpm42ZChe7PEVjU2d7cikxGEbo2JksHQCOZkYLICJ1NgMc1kFZgEGZqlCWdLQ6QlkR+4dbS6T6zFvxBpSPVJg1p/u09w/ieGqiHXFVWA6Pzk+3zcxVIRcdvp4RTmDMELOqSZy5zQG4Vr8NTx09teCwmqbMBzPS7AuKAQZkUHoMRb08FmoIgWi2vixA702htnAUFUiR6VQtCSk4ZsYgSFyExnXQS7FT1WJuL48ydqMxlbY+bK/GL+cnEGR46AUimnOARGF/Gpin9guhtHEMGra2q3YkGsYPr/0F9z5yCeyf/efidcKoiw3nMvefPzUM//iErXH9ngOX8tsc1++V5Knm1c56/x0BV0aGawqGW8AFkYpvwVlPKN09ijp2BiDRl0c4AihjVVHBhkdBkMPs+A4vjunB9Zo1OP8wvC/XmFTrgArlW0GDK+Kp57vgYXjyCLDEM+VV4XGEsh5d+j6rvQwjQWEPCcyKIKJFvWc9edp5aWGr+HcGbSStfv9R2mzhV27V7ymwTb6evjehMCfye6vBbmdLFJ7XL//osSQu04fAKu+NR8vSJ9JMesEJXtlZTkDhMNnFjhhgR+dwq+aWwrXIgHxHTUdG5io3RULb1bFZheFUuy0MShq4et588FE4YnaBJ5oGPUogZx3+GXR8Ng04liwodWqF/TSicLjNJSSPBmcogT2nIVzx3h5kfc1F4av80oYgyqRQbO5gALLiETKce5FoY001FCbyrnX5lJfDz0qmOtQFkS/X/ua4dTivyVBrisih6oJ1hglIgOr1sDwK8rBMlGWJMOoabBUhrOnybdopFdOkYzfTiAfCQ1k19CyyoalQ0g92aOUUk5k4Psz6YHVK3tg4cUXGTh+6K2v8sCZIm18cQy/HLap1dIKs/BymwQ/swI/z5mvYiOPF6BVieQnyNVcOt4cVVkhv3TprFFqyFcHNUTGIOKlr0e4bvXefLwgc66yzjXoFMJ7y/CT43NdLRJplMjBMjmDILrgJozqCvbJPOsWrkW4L28bg5VSVA5ZnMyKvKI5hS5xslTwVEKffbZBV85ze2BZtfgFHorgpw7ULQBtaApMwETFnmwQBKHHt1D4r3lF8WqiPNxZ8eotziuVQM6HFBXUYJrLGVaQlWBePtQA4DqikVtt3rnMOLhkZUQGcV598bmF9onkp520N/0agUH+eQ3JbylDrq1dOJd5CV1HRiL17cz3c8mSXUn9ADxpDMx6YZQcOSlLzOVtmGi1ZFmykVvBwgWBKxW0Mf/iZSxcBDdkRwaLGB7TTCioWGRQjDu7rlAoCylMDdqoUurp+xJ3NpuZUY+Z07M/JM0rip1AzsOdFzXiOq9EaalZUDrrLlUOGQ//xRmRgn3pu9L4mPNHdboCk1SzE3XsxNcjiuh68/ECDSZKrG/RyfFl+GUkkAvnMghWw0+D+IrkbpHIIFyLZhriO0o6HgnkkU3PbnNt1ozC8cxk7gEQSKx03sMv6YWLRwZGiuciSciwI2ZGw7NwU+ZFPcojqs3pEUE0vvc+ReM9j9O5/y7xd14UovHKinqSnT3T/LScQUc7Q5GHOy/jXeb1C/IKIgO5dmvrpxfglxUZKJgorVB0Qzd3PgTEGmXVqmuRgb4eYa5noShL/j7VGyh8Pd/vDMe3UAI5Hhk0mm2CAmPgeyOCwME025hW+graUmpY4vR4ePDMKjQ+85aVgrYWuiF3/Vwnb1V0PCKDX/lLrO//I3offRFGYOaWQ3qLhuKQUs6gKZWcfkiLYPgHB4dxfloVhVkSHkfjW8QYyGf+9ocxbs44855Pi7+eoDApmDOX4TjK+M1czKC8mshbtBRS52VHhhW0/FImFiz4zWYL+FOZOYMihbLEukF+XkeLsvT1iAx5bwFeOd5sgTHwQkO+hDGQytlxgkLIbSnoEjJaUhQnkBeJDGKykVGYclR0PCIDOaEqcRY0LLIC1hCXXchzzoeJ8prHLYLhq0tB0tVEjdLDNmUKupDySkhLohArR8DDcZTx+4k/pteAg7PSiyuBiZYaW7KaqKDyTI0PYwWwlKm1EsnqpeMssW46vyRpkYG+HgufMYBorhLrGzSMTJmL8VsmEglzIsWeemToFp1LM8ZPwFL5hRvzdhiAhGzULXHy3/bEhcFHSMcjMpCb3prJOt+yRNYiHlhGAllVE+U1dFvqsE2qmqhegpX6WmJwiXLIJC0YGZSS9lzDhs6Ns/L1nATyUjBRGgeG4mID5TiY1gL8kr2X9MggI6pzl/GcdX6p14sPgS2FqScoqGebAt93ZI8na/6T4zo/FYmUlXqG+7I3Py+YOzJwlylsgNTePEo6HsagoSID2awu13NeTYJVUapePXHqeRGPKLz4IlVN1ChUKCIK8TCtdUyz4MBXHuUojiAXkiqGGgovt8ngV5vItcstCe4DS3qzdoQDA7nVWUHghQpza/vu+fkl708wixPIS+1LyD1trxt4fT2W8tTzzp2kG/YC8VPjVevwM/lJuWs1O4WHwMoi1lLKrF4qyPc4N8VPqZ+szCImG7ewJcXxMAbNpDHI9lKWg4mySktVZKCqA/ISyNUjg6jXeToyqIKVLuyp55Qh5s9lMTZbeJ8BpJR+GIrnlUOGCmwB2EY3rEEgEt6GFbWIyMz1+Fi1TWaL1ICnKmBK1m6ZxH/OM4HYXOrroRTYYhFyTnVZTq52qTMNoM2lvOPcIzpvk+kUKX5LGgMNJio6Z7DIXMZkI+Og21HRMTEGYkJrU1FSlxeyusskWGsy869uXUJPRMrPJDZLtFFOVGajLnhPVqSYRjFMtFTyGPLD/2bOXJYYn3Acc/LLz1Go8VWfy5AsWVSgqjZIYsHJdVNOw1b5OLIoVb1UfOjMWxpyy8v3RK+rcQRBgGvfkPwWmMu8CDLnbN5SDhikIgPPNwrP27jL5gzq6cigqHBjkX0Z21PN28ZgtaRyBhNVX51T77zoYRSQ/XTSJYNAdHgp4dUuZXwyYaIjFII5hXz5RF25AlPk+470+KzFcG79uVmHl5LnQ5bZJ/rz7CyYKCsyWKLKTeenUWAEmVGW7w0JAhvTbGNZ6QviF+EFEDSzCwYcRxqeRnUYJc4v0bKhYN1gFdVEiYR1AUwk9uUBYC12/kXndztnsCJSxsARyjnIMQZRj5QlFdgsUtAE+f2QFsETu93EgaHMBPIRwES5Qp798SjqyeYXjmNOftnJ3IjXQnX4+nP1fkE5BxQjDH+7fByZvOYsLQ1x7iW9Z438hhs7JazGsbRytsxM7NxvZRuDKApZ1BgoB0zMZbPR1jz1AsO6sNOQrOKr5UJ8npasnmdfxvZUVmHKEdHxMAYJBVIWsi7sNSSSyKZ+4UzdjAlf5DWYc3kN4QGuVAVMWUXKkomzHCw4aASZr4dKpX4q8/3S9htzVMC4zpIKBTIiLQ0myo0MtuZvI6I/T98nBc3VjiIy8JtxT1ONIzKsS8xlViTSyFZmau3mcYhilLxT2qyVGNYljV3mPsmOyBeBgcUztT0V5ihuRwaroUQ4nKXAgsDTvOdFw/80dBN6KakzBrpCqe41DIdj8Y8k7myWeZfL5gxyYKJG2uPzvQm+N8QwGrmGLhzHnPyyjJLyLuuLCjhkeHz13DsGdJy7dByFvLLaUcRFMgg8be2W9J418pvxXIcaRziXqzCsGrknso2BY69IOct1s+2gWA7C8WU7KfPyK2pHsWgiPranMgpTjoqOpzGop42B6+xD4GHVeuG1jvPzSeYM8nv4uPZiXkNISZioQKEAuM518dbC2GxOlUg9bQx0qGEhzzmHX2D4mYrGWdADi/NLGPKCcxtLJ+PVGPQrS3MSyMIQqH25QEmwzk8jr5Gd+HZWEWVlGHL7rmx+K4sMQjmwcuXA80b4/kT0uprn3uoYvzScmGd8nGWNOGQ2GjwqOpbGwM/wZkNluQoh0KCbXGPgLuY1NJvqykJtUwZBXKFkeEShB7asR5SgoJnepK5dPpfhOPIoK1FctzObna0EJkrVjzfzE8haBFk6jiJeSoHRyD3FGkU9C64bZN697bbjEY0aRwSjLGFYE9HbjRe9m8DMLiUOveeFI4P4XNbqrfyITot6FnZSmnKdphEsZeYkkBeNDGJ76hZ2Lj2WxiALv4zC1SWELgXdNHIbrDn2Ythsuy0Lti1TKI4AcHxRWlrQvyeKDBYcX25kkN6kjuRVNLZwHHPw8xpHpFB0fhqOnzefesK6dByZvBJRSCALG6wgZeyiuVxiX2YY1uG9F2N/q3GoiHUlThHgnHW5+op3EATpyECcil8yqqtb4iIdR5R0Nxvt3LvAVxL1JKN/I7+d+6LQbGxP3YaJVkyJRfIzjMHSyhLSCeSiyGBBr6Hf15pYaVBRrK10MhLyZ3juAYZRW+yErs4rQX47KzIo92Zj48jkl2EM6lOCoIDfSqK6jBPdSaViRwq6dByZvBL7xNOMQYJCGGUVToqkmy94D4P7Hom9psYRRVkrigykcfP9tDHw3AOCwMWyNjDNBS4JUs/XkrrjiVMaGazG4dOi/5wrSxc9rxGX8dsw0WrJNAi0jeHXsyKDFXpgWmSQl0Be6pCUIq2SwjDrmI4IL4NUfiLalAsd+YeCyMBJvVYlMiilrBr4xozAz+K3ggqYpMdHA9O3xKUsmpD7vh2daVi0uic8oBiA52N6ct2y8i/2CuYyARPdePGfZnrqsILSUkjsdWEMggxjsBJICmIKOn6fQRK2WWFyXPWV8oURy4rqlo56IAUpHiUdD2MABJq3kqykAA3nXsZryOgkWh4ZzLdRLL31dqjAPHkxtzpHEf/OSqCGvJxBkFbOVSIDK6OFeIyyTlE3ZvgZSmwlSiURGZieDNXrxIRcXzfDsMrHkUWGEY1v5mLKO4KzjEG0L1dsWBPzaFmmOH28CgWm85POR9Y+cZY9Y5DkN3OxrGZBQlfKwSoMnTr570tDnuErOeHa3TEXi9ieuoX3IB8bY2A2o0oMr1Xgza4ihAxrnvVLWVYDE21uan2MkhszNAZxpbKSJGSGQhmeeSJTOVfBZmPjyOSXkQTvDAj8eN5gJaePIQXdWMrjqyUwfPsaECnn0nHkUTMK/015GNKvZUSsR2TIA98mCCJYanNzQ5w+9meLnz5WpHecRcFE6XzPSjx1iMnB2lovtwpsJXCiirJUAllFdbVEy+7AleMz5jZ2cRmPlyEfJR0bY+BpQu23MhRYWAGzCphIrybKrkhxFoxEci++8ANMr05AQGDFN45SYEt5RBme+lOv/s3s8N8uN6yll9tkHL6anLiS8jBXcvoYMiIDiWEn9Kib8PZKx1HGb+aFrbKDDPjyKAy5mKeAIIigh4ODw9UkWKEyTLQSSApiEN9o7BREBivMGah8jy+dvUS+R8DAPrX69twlwbE9dbuaaPXka/ih25ym3q+iwEop2adev7tXE5C41zAfv9jFF4rf9RFMhLD5NYeA+MZxnBUYOtOICdfhZ/RxO8OUkAuoodwDK73cxkxfEzo+/WSKn7sKTB3Sjf+kMUieSUmerC4dRx5pQq6MgV/LqsxaoQKTZBhybJq37nk+jn0VgHpzgas88/iFMFGW06CirPlglFx+M5cgqOcfAlsFLJXcJ352ZBA6YAvIXFzG407KUVLOMc+/2RRY8Y0pDqOMMczmYhdsKEodVa9ntjQQWKlPrX5i8YNEOr9feB/81ocAUYufVpgrUCggxqDd/QppIffcPkHginsTFrljVqe6Fc7l5c/9Y5y1w1RVilJgjeaZ5XlBOL5IyBMeX6jAlp3LaK+YjjQGiWR87CT3IhcgKUoZAwknJtbOmUlj0FjSGGTCRGljYM+uSH5Lrp1W0m0EdZH4J8DQjEHcSVlBbil0GuqAS2BlG4PlDd2tiwxWYgx2ds5tA28GPhu4AXzb7u75n63wvbcBnw7Ud3fPH+loa8+9A548ZHjmiZQQ6EfUFz6MApkXnWclkCOFMr/Q9XqaUtCF/EBEO37NgQSUEkYGq/DARmLu1MU9aeUsBLxRMrbYOPKoFfGz7xhBkKHA7BUpsMQZkbDcM2EMwuoeCW1UGkcmPz2BLL3nRGSgw3vL7cu4mCsHRF+7Xm+D65dWNZd6RJcfGax+7Vw2uz3Bz/JicyacFEc6KQs0F1RUt0S7c0+0Oxc9pdxcaHYRmYvtqZbUH5N0nnPVtCqY6EcBGzgNfCXwxp2dcy8u+sLOzrmv5BZGJuNPuZfxF9S4+JpfSSWzlAJb3iNKn0BWNc+BBlMts1EmEw3iyuo5U7NTSd0qJ4Irke7x5UQGobfXvLPwUbFx5FEripoM2SIkmUCO1m5J7zJV+58d/kfepYgMKo0jk18UGRjSGPi1hBEPDeuSY6uZ4pCiooy5nEymq1PO2rqpSqwsOHFlcqcZA3sslHLKsM6qOSnz8XNCp8FPGINkbmkeiu2ptmYMguymkKuipY3Bzs65LvAlwHfs7p4f7u6efyfwK8BXFXxnE/hO4F8uy78qzcwA5xVtWaueVGCXgXIFVkqpAylmhD1rbbOV0NUW2Cjxiy+yWjY4sVr8IHClN2ssxC9GWt4jNAbJyEDNZYkCq3QpTFszBkihS0Y9q8K5k4fOVGVWHhYs53Khy20gll9SN+H5tcUMayXSvHXTEMbADyJjMJvZqzMGbR36zIaJPPdAVC5Za1i1BfsEKVJyMHVxxkJh+lbcGNjK8CwLJ+r8Zl7k7K0wMojtqbolzkt4wZGXl67CM38e4O3unn9Ye+39wGsKvvO9wBuBK2UP39k59wbgDQCve90X8eCDDwLiyHatZjEYjACo12usrXXZ3xd30xoGbG31ODwc4LoeBweHbG2I4c5mQ/b2+nQ6bUzT5PDgowAExgmCIAifYZoGvd4mBweHYVJnc3Od6XQWLli328YwDIbDMdZkzCYQzDz29/oAWK4QvLEzwZav2RKbddwN9vb6rK11CIKA0WgCiN4krVaTg4OBeIZlsrm5Qb9/EFYabG1tYuOTPLfp1xyGB/tgTmg2G9y88Sii0dlJTLPB/n4/dDC2tjYZDkc4sj3u+noX1/VCz6TdblGv1zg8HAKwYUUbxpbXDPq+zWAwDJ8xm14SY/M22dvr566TGkdyndQcz2Y2tRra1bmC8+BwH8Ma0e122N8/YDIW/OqN05XXSc1xu90KT3s23BlrgDeecbDXx5/IaxRNhz25bt1uK0zoDoc1XG+M5/nh+/o6+X4QzvFoNMa2hRFbW+vi+z7g0wL83/4QPFe2gjCnHB4O2NhYZ3+/z+HB43JsZ2JzXLZO9XqN9fW1cK0NA7ZadRg7cs0sMGE6GTKZit8+m83CnMF40iYwh4XypK+TuqZRydPEmaJ61RrS33TdCf3+QShP0/Ej8rfewWg0rrxOao71te61apjApD9k2A/YRhgDtS7NZiPcl16wxcHBYeV1Go+FTLZaTZrNBgcHAzZqptiNU4fZwKYDeJYo1VXypPalF2yGv6PSOm31GI2ivlEbG2tYrTqGM2P/0k1apzfn0ntqnSyrvNJuFcZgDThIvHYAZGZid3bOfRzwauCbgdLbxHd3z78JeJP8MxUnbW/3Cv/e2BA/Y22tgz0RXmutFsQ+Z5n78jP3YRhG6hnJWvJut0O320nwbYCsYjBsN3zGQMIN7Y0Wa/K1CzdFCLnZu49NjVezGVftyd/R623S6bRpNMQzm+vx3wDCGKyvNeh0BC7abQ+5DjRbZwGxUXRaX497ZfV6PdVvJ/wdnUg1N5uCdxDYsWc8cUN4RL2tZ7Gh8UqO5a67zoTjgGidFHU6bdiMxmfWmuBBt1Nnba0bPvPq46r08jTtbsV1yhqbXDtr5rO93WNoye81or3i2NchcLFqPU6cFB5mvV6LjQPEOumkfm/8Q+I18+oQ86pQDkHdCedha6vHaE/sy0bzzvnWSVJsrbVTyLV6GwJo1KG7IT4zm03Zf0qs3clTzwmT/3nypKjTaYd7TVHjtHZ2RsJEpuGF87K5uYHhC4VXb9453zpJislkpw9A2zc4syWSw37NZmtrPYxgD+W+XN+4L/xulXVqtTJkcr0pKvjGDp1aFxhDQ+Qo1DpdvyA6DKyt302j2Us/Q6OkTJ4+fTK+p9p1GMzYarWhnb0uZetUhUqNwc7OubeT7+W/C/hnQDKLtgEMMp5lAj8GfPPu7nl3Z+fcXD92GQqCQNw8Rj600Vg2HM8oAzNlZODXPJRtjsLx+UNI/aBQ1sGsoObEavFXCjVkwkRJ2EbBRMX8gir4pwY3ZEEbnjfC9wby3oRe+fOKqKOwWZkgD8P/CG6wZ8LbazTPhq9VGkcWtdNVZL4Vz4esDL5M8DONOgTxU8iuc1P0CaptLl8FlgETpWVO7csVYPgdDVeXsufXRFWdIZPFK8stAbSloh7bYb7HsyI58H1b9jqzqNeXlHGI5w2OkEqNwe7u+U8rel/mDGo7O+eeu7t7XnW/ehnwUMbHN4CPA35eGgKlzZ7c2Tn3Zbu75/+g6g+fl0ajCS1pbdNJzxUJXUYfkbAqRatXXwabHY0mUQSR0TzOa0wxNcFzbAWjrEAIYglrlUCOFFgQBJphLeYXG0ce6TkDWQGjK5UwP9E8s1y1DcSrNvxA1v57MWPgZJRCVhpHFnUaqZe82iT2d2RYV7B2Gj/DbIAfTyArqLTRvGt5XrFcj8wZpGTuKcGvxGmYi9/YYXYwpoHInfmBjYk0BiqBvIqcgWZ8svI9Yl8G1JunMcz5wZfUnnqmGIMy2t09P9rZOffLwHft7Jz7euDlwGuBT8r4+AFwVvv7HuDdwCuA68v+ljJSl9boySzfm+K5+xhGbbl+LBA/Z+AHYBrhCWSViPS8EZ7bxzAaq+OnkdecYGVEBro3uwp+pilr47W59LxDfH+CaXYwlzmvoUhXKqo2XotE7Klow9xo3rM8L8sU45u5MHUwpTHwtBbdtp2ODBamTjoy8KxR+G/fn4nT1Ya1XF+iDH5RZZa2do5Uzq0VzKV+8tcJUrwAZnLtmu17l+enDF0sMnBiPO3QAVtFZBAZn8gYRIY1NHSr2CcQj3yOkFZV2vkNwH8DrgE3gX+yu3v+IYCdnXP3Ah8EXrS7e/4CWtJ4Z+ecikevHvU5g2azkQkT2ZrnvHBHT0WmITL/ji8y/81a1C9IlrrZ02ijLMIvdvFFBkzkNacxhemsEmrQvVl5ziA2l0o5t+4q9dQrXQoTMwYKJor4hQplFQoMhNDNXOHx2VkeX7oiZaHLbSATJvLqIwLfxTBrWlXW6fBMx1KUEWXphjzw1eG90jTefCR7+ASBLaFaMa8rNeSa59wIonVTkY/nDgWcaDaXhxMhZnwMKWq+dpA1MgaLRVmpPaUZn6OklRiD3d3ze8Drct67gEgyZ733OApUPGJqtZp4rizz1BSKPbkAQKO1Ag8FBHTj2EKpNGsh9qyUSrhRWottlFhCK+ty+MY0Pr5wY67CGOjhvzQGMeX8BADN1v2lj0om5jJJx7kzYCJ79iQAjdaKFFinAfsT4fHZsva/rhuDdGRQaRyZvDKMQWOG70+xzDVmU7Evm6valzpMRPoEsu9ekvxWZFgVr4mLYdQJAocgsDGMJr4/kxi+tRpYSvOc6740BnUnNHb2LDI8S8OJoClnG8MVz/Os6GzAssYgtadu0cGzY9Ob6OBgoMFEWn11qMBWJHSJroam7DvjyeSg2ij1BTeKKjcFsr1LLTLw3CGucwPDaCxfOw5xhWIomEiDbeYwrLFx5JE+vjCq08LxMBJZ0dppHqaKDLxY+J+OsiqNI5NXRs6gMcP3Rd5AGYOVjS0DJtLlYBqu3Yojg4mDIS+uUfxEdByI6HgBTD1FmrKc9kVlll+zw3xWZFhXGEGC8NRtda5BMwbT5eDE1J5S+3J62xisjLJgolDo2vethkk3qjTADzBs1YRMCLkTeg0rELpuWqG4nUFYTaR7l0t19MziZ6QTyCv3ZrMSyFo+JISJVgVtdDSPL4SJxLr5vi0PEpkrMqzZhtz3hVKxV+2kaIbcTJxAFtdPqsT/ihSmll9S7bB9T5ST2qGhWxEv0wj3ijmIGjaGkUHoNKyIn+40yJyIX9MjAxGx1heM/ov4HSUdG2NgWWaUONNhlKMKx0cCKjIw8Go2PkKphOWJC26U2MUXGRed2+v9EFe3p48LXqsydLp3mZEzCKOsCvwqXQoTg4niyX/PHYrEv9lcvgGfIrV2YwdDDsuzlAK7CHg0mnfFrmhc6HIbyC4trc8IpDGYTZQxOIq1i8NEjn0NAhurtrX8aeAMfqY8s+F7IkEeReMrhKTkfFqH0sBpCeTV55ZUzsAGWxaGWOJKVt93pDEwaDYX0ympPaVHIkdIx6Zr6ebmhqzfNQgCV96lazCbPA6sUOi6mjGQUJFfn+HLowdKyBeNDGKHbTLwz8DyQpho9QpFh4lUo7pZ+H97+iRgVoI2Kl0Kk1VNJBXYbPIYIMa2dOI/yW9iR8agNpT8ROlls31/7CsLX26TZUQMUd0WBMHqYaK2HhnEYZvZ5CMAtDrPXg0vEHO5P5H8lDEQhnU6Fvyaq+Qnqd6XDRvrEUxkHxXkNnbCHISIRKbiFHfg0WjevfB5jdSeakdOylHSsYkM+v0DDMPANEXdse+NcWaX8P0xtfqJxe+zTVLMGEjsvmHj+xNc9xDXuY5hthbGE/v95GHviPb+kRC2UGHKyOBojIGqSJEY9+QxwKPZurfS5eZF4whJNwZBPIE8ke0MWp3nVPrplUgXcokFO6bAb2fTbGNQaRwVKDBUe48Jjn0V3xtg1XrLlx8riuUMIhkAmI4fBaDVXqFy1iBFU8JEni8ig+lErd1zV8dvT7ZwkKe5vZojDaurOXwrhi/HTtjl1q85+N44clLaDyz8+NSe0nXKEdKxiQxUDxLTWsP3x3jeKOyPstJN2ZWKMBEZeJ7NLBS6Bxb2ZtU4QjIIm3QEJ2owjmrxVz6+VDWRReDP8H1HU87Pq/So1DiySIt8TCue9Ay92fYK166j5XukkLvWIUEQMA0jg7iQVxpHJYquh9TXbSXVLxDPGchTuZ6EbZSnvlLD2tWhNJUzGOF5Y+zpkxhGjWZrcYVZRn5jiueNsKdPEgQ29cZprNoSd0LopO8TCdX6dRvfGzMNjcGzFn58ak/dsQb/6BNhY8mT4SV0bIyBIqvWxXXA94aaAlulMUhHBn7dxvd9phNhDJqr9MA6jdBjUEIuopADHPuqiEJWVSGiQRvG1MXqreG5B/je8GgMK8A/fw0cTjF6T8I18H3lzR5BZKDWbmhjTJXHNyEIHM27PBoFNnxQ9Fjy/UmY8KxqWCuRZsgtQyln4UVPpWFttlc4l2vpyMD3RtJzDmi27l/uYqcS8hpTfH98NIauYUXnifqywEDyU3Bia4nIIEXtOrx4BYflSujYwERbW6IplbrJzPOG2kY5CmMwCyMDrz6LhZDLhONqHCHFhE6OzR0wHT0c8lpJJVGSxnYU/nujiF+3mgJLjSOP7t+Gl56N1s0dEARuBG2sUsjXpDc7nIX3Gvg1B889kPkQI5UcrzyOEjp8jWhs5vvT0NC1V7kvNUNu+QLD99wBvjeVY7NSENhSFIOJJD9vfHROw2fE94HXnOF7o6MxdIYRRQfyxLMnI4Mot7R4ZLCqPTUvHRtjoNrCRl7KkOlYKrBVbsy1vMhgwmT0Iclv8Y2pt7cFYkJnWaISxPdG2thW6F3Gfogd8vPcQ2YTpZyrzWVqHCVk1pQRHxxN6A/C2wMY2qKRW80BM2A8/EvAo9G6J5UPmXccMbpP5qk+87mYDXkg0psencL8whfDK+/BONMD0JSlT715T1ixtRL6BGk0P+4eLCtKIE9GHwSg1X3+6ngBfGw8+vUaUwHbHIXDB7HS2cD0CSwXx7mJPRMQ2DJlrEvtqSXo2MBEqk+5UmDT8WO4znVMa221R/D10tKJMgYzXPu6FDxjKUFQ4wgphs1K5azDNhU99blpOAsN63jwPnx/Qr15llqtmleTGkcJhYbOHTIeiR6Irc6KFYryngeiIsVrit84HrwXgHb3hamvzDuOGP3jT4KHrsDL78K89k75vMs49hUMs7m6unhFnyX2giVv4fK8gTR0UGuseC5PdOEHvxDqFuYV4Zj43ojx4AMAdNZeslp+m3E83WvMZF7wCGAiiPVf8pseGEIOIKDZfvZShnWpPbUEHRtjoMiUSmV48EeA2JQrK02ERGQgYaKGHXpEzfYDYUJtJaSH4zVlDAahwmwfVWQwdUPoZnDwLvFT1l52NLwgrH/3vCHjwV8A0Fl7cMVM5D5Ql8C0xPqNDqUxWLXx6TbglaLCRV14P+gLo9Dpvvho4D206h5vyEQag0brBatnJNulKH727Ens2UUMs7XaxD9EEB8Q1ADTx55ekA5fdzVtL3TSzvgETZHwHYVOwxHM5S2gYwMTqYsrlCIeD5VCeemKGclNOZiJ/xCdRBUtq8BSF3DcGcEkynt2ZpdwZpcwre7qPaKveyVsteFLXxrN5eB9AHTWqxuDzAtfCsgwarIs2Gd48CeS34rXrhYXh6AthFyV6LYyhHzeceSyrveAqP9RZ/3lK3luFol5FJVgI7l2ve2PPTJ+tVrS0L1oNW0odNLPbViiAiuU8e6KHT6AregynkCKvEr8L2sMVrWn5qVjExmIqwZJYczzKLBKVLdE9n/iwHVZ89yOwr61jVcu9Xg1jpA+5Vlw+RBedjaMehR11l66mo6XOr3sLvEfYF1IzuXLKz8mNY4KZNU28O2JOKthNFafDzHjZZxBO/LMDaOWGWUtMo4sSnbTPEpjYBgGVm0dz+3juftYtR5WfcWes0bJu7e7Gx9/ZLyAsF+QopU7DQDbmjHoxA3Nsmu3qj01Lx2byEDdZaq3HzbNNu3ui1fPTF0Csy8SQQpuAOhufNxSj1bjCKlmwt//WHjxGUyzHobkAN31j1mKVxnpDdvqjTNz5V5S46hAusLsbrxitQlPiEENQKwcs732YFi6q9Mi48giS8u1GEaNTvdFK3luHul9/dc2X8VkMiv49LK84u1C1nuvPhpGnymgJ/+T4r2jVg4nQswYsBHtm1r91NL9nVa1p+alY2MMFOnXMa5tvupoap1VcknWIDdOCqim2X42Vm0Fl74UsdaEvLt5tB6Yfop6vffJqzsglcsv8l7Xe1l3Jy1J7Xq8Z1A3Skqubb5q9fw00p2UzvrHZBqeVZLe0vyox1arb2v/PrFU2WUhffbzGX7xCzG+4OXhS4ZRo73qZDUkjIHmgG284sjl4Kjo2BgD1SO81Xm2VCoGvZNfcDTMVImiI8K93v1fzOb2Z3HPc75n6UeX9c9XFS+N5t2rT9IlqLP+curNsxhGnd7Jz53ru4vcAxD1zjFY3zwi7/JEJOT1rbOok8Gb25+V/ZsWvc8gQfX6Cda3XgOYbJ963UqeWURqnxhmk7XNT1zZOLLIMGrccfc/xjBqnDr7tUenLNt1zFfdh9nthE7f2uYnVWqPMjdpxsDaPhW2Z+md+JylH32Ua1FExsIXej89tPCP9TwPy1I9+Gd4Tn81l3Fn0Q+/HR7f1/5+bSo5uSjp48h83x3Sv/EbdDc+ZvW11RnkexN8fxLz/qpQ2TjyeF2//P/SbN1L7+TnzfXdyvRf/xj+QrRz5vWvZPCsi1jWWm4p5CLjyKMg8PG94WrPTuSQ79vsXftlWp3nsrbxipWOI4+CwF19DitBahyjw/fQv/nbnDr7+tXce5wkP4B/+avi0Nk/+kTG993A98asbS6XE4TV7imNSi3wsTEGe3t9trd7K/wpBfRTfwrvET3NadfhP6wuArml4zhCesaO4399AH5P1Kaz8xp4oNjIPWPHMSfdHscC9IHLcOkQPvt5mR2EF6UjGkPpDzw21US3lO7Qqnq2jhb7vU0rplO31+42VaQH7xT//Q2hY2MMarWjDYFjpCuU3moVyi0dxxHSM3Yczz8lfKj1ZupUaxY9Y8cxJ90exzOHnq4xHBuY6JbShX34wbeLf7/6Afi7L///t3euMXZVVRz/aTtiw1ihgjXatE0biloVQjX1C4pSiRpFQknkpVaDRZqpYUk2oE4tYSBFN7gSjS1FCwGLja8Cllei0WqKhqTxUToBimirBRFqh6a3DGVaxw/7HHvmch9z79x7zz531i85H+7ZZ++z/nfdmXX2a508rTEaZff+sDv45N761xpGMag7TDRpVhMNDb3YuZtldgWPGTJqAR3V0Uai1jF3xrgDQdQ6GsB0xENeGiZNMOhoB6hnCnzynWHy8YwWJsGjwzraiOmIC9MRD3lpmDRzBh3nI6eGwzAMowBMmjmD0dHRwu4MzGI64sJ0xEU36GiTBpszSCmVDuVtQkswHXFhOuKiG3TkpWHSBIORkSP1LyoApiMuTEdcdIOOvDRMmmBgGIZhVKclcwYibgawATgH2Ad8VdX/qMb184DvAB8EDgO3q/qrx3Grpo0dGRmhp6cNGUo7jOmIC9MRF92go00aOjZn8D3gFWAmcAmwTsRVfFGAiHsd8Evg18BbgFnAxhbZUZUjR462+xYdwXTEhemIi27QkZeGCS8tFXHHA0uBd6n6ErBNxP0C+AxwbYUqy4BnVf23M+d2TNSOegwPv8y0afXTC8SO6YgL0xEX3aAjLw2t2GewADiq6ndlzv2FMARUifcDu0XcQ8D7gJ3ASlX/WAtsMQzDMJqgFcNEvcCBsnMHgGqv9JoFXEiYM3gr8ABwXzJ89CpE3HIRtz05LieMfTV8DAzc2HTdmA7TEddhOuI6ukFHmzTUpW7PQMRtpfpT/iPASqD8bRzTgYNV6gwD21T9Q0n7NwP9wDsIPYoxqPrbgNvq2TkOlreonbwxHXFhOuKiG3TkoqFuMFD1Z9UqT+YMpoq4U1T9U8np04DBKlV2AG16Z6FhGIbRDBOeM1D1h0TcZuB6EXcZcDrwKaDaG8s3AleJuCXAb4AvE5ajPj5RWwzDMIzmaNXS0hXANOB5YBNwhaofBBBxs0VcScTNBlD1TwKXArcCQ4TAca6qf6VFtlSj6F3HFNMRF6YjLrpBRy4aipaozjAMw2gDlo7CMAzDsGBgGIZhWDAwDMMwmARvOms0iV6bbdlK2IGd5qh9RtWfmpSdTcjxNBt4FFim6vckZa8BbgIuS+ptAK5R9aNJ+VzgDmAx8A+gT9X/KnPfi4E1wEmEvFBfUPX7G7C7j5BG5N3AJlW/LFOWi90i7jhgHXAB8BLwrbIUJ+PSkNjwdyCbRP6bqn4gNg2ZOmuBJcAM4K/A1zL7dorij6o6iuQTEbcROBs4Hnguuf4HSVkhfJEyGXoG406i1yH6VH1vcqSB4CRgM7CK8IexHfhxps5y4DzC/o33AJ8ALs+UbwL+BLwJ+DrwMxF3ctL2QmA9IVfUTMKPY22DNj8L3ADcnj2Zs93XAacAc4APAVeLuI82qiHDCRm/DESqAcID3D8JG0HfSPjufyLi5hbMH1V1ZK4pgk/WAHNV/XTgXOAGEbeoYL4AujwYZJLorVL1JVW/DUiT6MXE+cCgqv+pqn+Z4MzTRNzbk/LPAbeo+r2q/hngFsJTLiJuAXAGsFrVD6v6nwOPEXRDCIBbVP3vkkSCq4DzRVy1dCGvQtVvVvX3Av+JyO7PAgOqfkjVPw58P227QQ31iEZDouOQqr9O1e9W9f9V9fcTnqIXUSx/1NJRj5h0DKr6w8nH0eSYT4F8kdLVwYDqSfTy7BmsEXH7RNwjIu6s5NxCMqk4VP0h4GmO2TmmnLEaFgJ/U/UHa5Rn236a0FNa0AItudgt4k4k5LWq1nYz7BFxe0XcHclTXUrUGkTcTIIvByvcrzD+KNORUgifiLi1Iu4l4AngX8CDFe4TvS+6PRg0mkSv3VwDzAPeRthYskXEzae+neXlB4DeZNyx0brl5RMhL7t7M58r1W2EfYTsuXMIT6VvAO7OlEerQcT1JLbeqeqfaMKeKLRU0FEon6j6Fck1ZxKGhg43YUfuvuj2YFCisSR6bUXVP6rqD6r6w6r+TkKiv49T387y8ulAKZlsarRueflEyMvuUuZzpbrjJhk+3K7qj6j6fwN9wDkiLm07Sg0i7rXADwlPhH3juF+UWirpKKJPVP3RZBh6FnBFE3bkrqHbg8EukiR6mXO1kuh1mlFCetlBgl3A/+c65nPMzjHljNUwCMwrmwMoL8+2PQ84jvDdTJRc7Fb1Q4TueLW2J0K6JT9N+xudhuTpcQNh8nCpqh+pcr+o/VFDRznR+yTDVI5954XxRWp416KNJ9FrGyLuBMIysd8SlpZ+GvgAcCWwH/Aibinh/Q7fAHYkXWaAu4CviLgHCX8YVwHfBVD1u0Tcn4HVIq4f+BhhdUI62XQ38AcRdybwR+B6YHPZeGQ926cSfitTgCki7vWJhntytPsuoF/EbSf8M/ki8PkmNCwCXgSeAk4kvGdjq6pPu9nRaMiwjpDyfYmqH86cL4w/aukQcYspgE9E3JuBDwP3E1LzLwEuAi4Gfk+xfNH1PQOokUSvw/QQlja+QBgTXQmcp+qfVPUvEBx9IyF532LCC4BS1gNbCCsKdhJ+XOsz5RcC703q3gRckLRJovVLhB/Q84SxwxUN2t5P+LFfS0gyOAz052z3asKE3B5CgPWq/uFGNRDmcB4mdKN3EsZ7L4pUAyJuDmEJ4unAcxKSQJZE3CVF8kctHRTHJ6OEIaG9yb1uBq5U9fcVyRcplqjOMAzDmBQ9A8MwDKMOFgwMwzAMCwaGYRiGBQPDMAwDCwaGYRgGFgwMwzAMLBgYhmEYWDAwDMMwgP8BCfBxmhTTm9wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(model.predict(states))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f051bee1-1f8b-4acc-a658-d80a5b4df54b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_episodes, test_episodes = train_test_split(dataset, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1025ba17-f95f-426b-adc1-9312e5e180fa",
   "metadata": {},
   "source": [
    "## Setting up an Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f5ef782b-6847-4fa4-8d95-3fa8904866bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from d3rlpy.algos import CQL\n",
    "\n",
    "from d3rlpy.preprocessing import MinMaxActionScaler\n",
    "action_scaler = MinMaxActionScaler(minimum=-0.6, maximum=0.6)\n",
    "#cql = CQL(action_scaler=action_scaler)\n",
    "\n",
    "model = CQL(q_func_factory='mean', #qr -> quantile regression q function, but you don't have to use this\n",
    "            reward_scaler='standard',\n",
    "            action_scaler=action_scaler,\n",
    "          actor_learning_rate=1e-5, \n",
    "          critic_learning_rate=0.0003, \n",
    "            use_gpu=False) #change it to true if you have one\n",
    "model.build_with_dataset(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2fd44e0e-0ab2-4ab0-8f96-7a8eb382cda0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.23601703744964858\n"
     ]
    }
   ],
   "source": [
    "from d3rlpy.metrics.scorer import td_error_scorer\n",
    "from d3rlpy.metrics.scorer import average_value_estimation_scorer\n",
    "from d3rlpy.metrics.scorer import initial_state_value_estimation_scorer\n",
    "\n",
    "# calculate metrics with test dataset\n",
    "ave_error_init = average_value_estimation_scorer(model, test_episodes)\n",
    "print(ave_error_init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8f69d169-0f59-4e77-8654-c5d79dae2b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "      <iframe id=\"tensorboard-frame-629f6fbed82c07cd\" width=\"100%\" height=\"800\" frameborder=\"0\">\n",
       "      </iframe>\n",
       "      <script>\n",
       "        (function() {\n",
       "          const frame = document.getElementById(\"tensorboard-frame-629f6fbed82c07cd\");\n",
       "          const url = new URL(\"/\", window.location);\n",
       "          const port = 6006;\n",
       "          if (port) {\n",
       "            url.port = port;\n",
       "          }\n",
       "          frame.src = url;\n",
       "        })();\n",
       "      </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9b0384d5-a843-4d30-a355-404215f27fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-07 22:06.12 [debug    ] RoundIterator is selected.\n",
      "2022-04-07 22:06.12 [info     ] Directory is created at d3rlpy_logs\\CQL_20220407220612\n",
      "2022-04-07 22:06.12 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-07 22:06.12 [debug    ] Fitting reward scaler...       reward_scaler=standard\n",
      "2022-04-07 22:06.12 [warning  ] Skip building models since they're already built.\n",
      "2022-04-07 22:06.12 [info     ] Parameters are saved to d3rlpy_logs\\CQL_20220407220612\\params.json params={'action_scaler': {'type': 'min_max', 'params': {'minimum': array(-0.6), 'maximum': array(0.6)}}, 'actor_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'actor_learning_rate': 1e-05, 'actor_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'alpha_learning_rate': 0.0001, 'alpha_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'alpha_threshold': 10.0, 'batch_size': 256, 'conservative_weight': 5.0, 'critic_encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'critic_learning_rate': 0.0003, 'critic_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'gamma': 0.99, 'generated_maxlen': 100000, 'initial_alpha': 1.0, 'initial_temperature': 1.0, 'n_action_samples': 10, 'n_critics': 2, 'n_frames': 1, 'n_steps': 1, 'q_func_factory': {'type': 'mean', 'params': {'bootstrap': False, 'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': {'type': 'standard', 'params': {'mean': -0.04222798673762325, 'std': 0.029288196098310567, 'eps': 0.001, 'multiplier': 1.0}}, 'scaler': None, 'soft_q_backup': False, 'target_reduction_type': 'min', 'tau': 0.005, 'temp_learning_rate': 0.0001, 'temp_optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'use_gpu': None, 'algorithm': 'CQL', 'observation_shape': (6,), 'action_size': 3}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a703eb1efa224772881081c4ca60af27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1/5:   0%|          | 0/8590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-07 22:50.17 [info     ] CQL_20220407220612: epoch=1 step=8590 epoch=1 metrics={'time_sample_batch': 0.0009770955140154909, 'time_algorithm_update': 0.29735887328182303, 'temp_loss': 0.3410345619932528, 'temp': 0.9888876052965802, 'alpha_loss': 8.785565134753224, 'alpha': 0.7231650749622596, 'critic_loss': 36.18224927438846, 'actor_loss': 24.133028367034033, 'time_step': 0.29970271362553375, 'td_error': 71.8260358979081, 'init_value': -48.7784538269043, 'ave_value': -48.87078756352829} step=8590\n",
      "2022-04-07 22:50.17 [info     ] Model parameters are saved to d3rlpy_logs\\CQL_20220407220612\\model_8590.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "687fcdf20fa34dbf9ec75abeab683191",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 2/5:   0%|          | 0/8590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-07 23:40.17 [info     ] CQL_20220407220612: epoch=2 step=17180 epoch=2 metrics={'time_sample_batch': 0.0008257755439411914, 'time_algorithm_update': 0.33884366982474456, 'temp_loss': -0.00011581030248190526, 'temp': 1.2431983920276095, 'alpha_loss': 4.4406926053783256, 'alpha': 0.32912610182072427, 'critic_loss': 193.84284772006959, 'actor_loss': 61.46111132096634, 'time_step': 0.34103232834475145, 'td_error': 123.44744246701725, 'init_value': -60.01475143432617, 'ave_value': -60.1333289133089} step=17180\n",
      "2022-04-07 23:40.17 [info     ] Model parameters are saved to d3rlpy_logs\\CQL_20220407220612\\model_17180.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2f0576e3717d40d0b786713862d7dbdf",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 3/5:   0%|          | 0/8590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 00:30.10 [info     ] CQL_20220407220612: epoch=3 step=25770 epoch=3 metrics={'time_sample_batch': 0.000880347724843497, 'time_algorithm_update': 0.33793379966260667, 'temp_loss': 0.022156629652978595, 'temp': 1.1385223336908121, 'alpha_loss': 1.5525975524234272, 'alpha': 0.15624857592430047, 'critic_loss': 254.6554936030691, 'actor_loss': 63.8807864867489, 'time_step': 0.3401938022640171, 'td_error': 138.5722329446111, 'init_value': -58.860687255859375, 'ave_value': -58.989980799579456} step=25770\n",
      "2022-04-08 00:30.10 [info     ] Model parameters are saved to d3rlpy_logs\\CQL_20220407220612\\model_25770.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "594431fd9d7244ee8f154809ffa6b7bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 4/5:   0%|          | 0/8590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 01:18.56 [info     ] CQL_20220407220612: epoch=4 step=34360 epoch=4 metrics={'time_sample_batch': 0.0009392525458641186, 'time_algorithm_update': 0.3300192184581468, 'temp_loss': 0.03313248623246533, 'temp': 0.9043158824979496, 'alpha_loss': 0.5097132887270592, 'alpha': 0.07484302060620054, 'critic_loss': 248.88140230060873, 'actor_loss': 55.150059736427245, 'time_step': 0.3324029747626557, 'td_error': 122.52313128510093, 'init_value': -47.87778091430664, 'ave_value': -48.00197334582618} step=34360\n",
      "2022-04-08 01:18.56 [info     ] Model parameters are saved to d3rlpy_logs\\CQL_20220407220612\\model_34360.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6a13bc750ba4ebe9d7735f35cc786ca",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 5/5:   0%|          | 0/8590 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:06.50 [info     ] CQL_20220407220612: epoch=5 step=42950 epoch=5 metrics={'time_sample_batch': 0.0009074546960513167, 'time_algorithm_update': 0.3238304439329296, 'temp_loss': 0.023459250246590964, 'temp': 0.7026218292876922, 'alpha_loss': 0.17984766460330648, 'alpha': 0.03568290511516982, 'critic_loss': 201.15485333642803, 'actor_loss': 41.53012437309736, 'time_step': 0.3261824154048915, 'td_error': 93.56227844641366, 'init_value': -33.98887252807617, 'ave_value': -34.097401090584675} step=42950\n",
      "2022-04-08 02:06.50 [info     ] Model parameters are saved to d3rlpy_logs\\CQL_20220407220612\\model_42950.pt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(1,\n",
       "  {'time_sample_batch': 0.0009770955140154909,\n",
       "   'time_algorithm_update': 0.29735887328182303,\n",
       "   'temp_loss': 0.3410345619932528,\n",
       "   'temp': 0.9888876052965802,\n",
       "   'alpha_loss': 8.785565134753224,\n",
       "   'alpha': 0.7231650749622596,\n",
       "   'critic_loss': 36.18224927438846,\n",
       "   'actor_loss': 24.133028367034033,\n",
       "   'time_step': 0.29970271362553375,\n",
       "   'td_error': 71.8260358979081,\n",
       "   'init_value': -48.7784538269043,\n",
       "   'ave_value': -48.87078756352829}),\n",
       " (2,\n",
       "  {'time_sample_batch': 0.0008257755439411914,\n",
       "   'time_algorithm_update': 0.33884366982474456,\n",
       "   'temp_loss': -0.00011581030248190526,\n",
       "   'temp': 1.2431983920276095,\n",
       "   'alpha_loss': 4.4406926053783256,\n",
       "   'alpha': 0.32912610182072427,\n",
       "   'critic_loss': 193.84284772006959,\n",
       "   'actor_loss': 61.46111132096634,\n",
       "   'time_step': 0.34103232834475145,\n",
       "   'td_error': 123.44744246701725,\n",
       "   'init_value': -60.01475143432617,\n",
       "   'ave_value': -60.1333289133089}),\n",
       " (3,\n",
       "  {'time_sample_batch': 0.000880347724843497,\n",
       "   'time_algorithm_update': 0.33793379966260667,\n",
       "   'temp_loss': 0.022156629652978595,\n",
       "   'temp': 1.1385223336908121,\n",
       "   'alpha_loss': 1.5525975524234272,\n",
       "   'alpha': 0.15624857592430047,\n",
       "   'critic_loss': 254.6554936030691,\n",
       "   'actor_loss': 63.8807864867489,\n",
       "   'time_step': 0.3401938022640171,\n",
       "   'td_error': 138.5722329446111,\n",
       "   'init_value': -58.860687255859375,\n",
       "   'ave_value': -58.989980799579456}),\n",
       " (4,\n",
       "  {'time_sample_batch': 0.0009392525458641186,\n",
       "   'time_algorithm_update': 0.3300192184581468,\n",
       "   'temp_loss': 0.03313248623246533,\n",
       "   'temp': 0.9043158824979496,\n",
       "   'alpha_loss': 0.5097132887270592,\n",
       "   'alpha': 0.07484302060620054,\n",
       "   'critic_loss': 248.88140230060873,\n",
       "   'actor_loss': 55.150059736427245,\n",
       "   'time_step': 0.3324029747626557,\n",
       "   'td_error': 122.52313128510093,\n",
       "   'init_value': -47.87778091430664,\n",
       "   'ave_value': -48.00197334582618}),\n",
       " (5,\n",
       "  {'time_sample_batch': 0.0009074546960513167,\n",
       "   'time_algorithm_update': 0.3238304439329296,\n",
       "   'temp_loss': 0.023459250246590964,\n",
       "   'temp': 0.7026218292876922,\n",
       "   'alpha_loss': 0.17984766460330648,\n",
       "   'alpha': 0.03568290511516982,\n",
       "   'critic_loss': 201.15485333642803,\n",
       "   'actor_loss': 41.53012437309736,\n",
       "   'time_step': 0.3261824154048915,\n",
       "   'td_error': 93.56227844641366,\n",
       "   'init_value': -33.98887252807617,\n",
       "   'ave_value': -34.097401090584675})]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_episodes,\n",
    "        eval_episodes=test_episodes,\n",
    "        n_epochs=5, \n",
    "        tensorboard_dir='runs',\n",
    "        scorers={\n",
    "            'td_error': td_error_scorer,\n",
    "            'init_value': initial_state_value_estimation_scorer,\n",
    "            'ave_value': average_value_estimation_scorer\n",
    "        })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bbb0460-f723-4b67-a553-57887d11dbc9",
   "metadata": {},
   "source": [
    "## Off-Policy Evaluation\n",
    "\n",
    "We do get some metrics on a test set of initial state value and average value. However, these estimates (using the critic's Q-function) of model performance are biased. They're useful for validation during training, but not much else. Instead, we fit a Q-function to the data (or a separate dataset, as I've done here) separately and evaluate the model's performance on it.\n",
    "\n",
    "Feel free to change the chunks and number of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "de79cadb-28ef-42f8-b230-756f3ee11bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -7.69891077e-02  4.00000469e-03\n",
      "  5.99998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.50295370e-01 -2.41931634e-01  6.00000000e-01]\n",
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      "torch.Size([2777000, 6])\n",
      "2022-04-08 02:07.41 [debug    ] RoundIterator is selected.\n",
      "2022-04-08 02:07.41 [info     ] Directory is created at d3rlpy_logs\\FQE_20220408020741\n",
      "2022-04-08 02:07.41 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-08 02:07.41 [debug    ] Building models...\n",
      "2022-04-08 02:07.41 [debug    ] Models have been built.\n",
      "2022-04-08 02:07.41 [info     ] Parameters are saved to d3rlpy_logs\\FQE_20220408020741\\params.json params={'action_scaler': {'type': 'min_max', 'params': {'minimum': array(-0.6), 'maximum': array(0.6)}}, 'batch_size': 100, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 0.0001, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'bootstrap': False, 'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': None, 'target_update_interval': 100, 'use_gpu': None, 'algorithm': 'FQE', 'observation_shape': (6,), 'action_size': 3}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30eaf664e3af4feeb50c7df7117642a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aero\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\numpy\\core\\fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "C:\\Users\\Aero\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:11.14 [info     ] FQE_20220408020741: epoch=1 step=21992 epoch=1 metrics={'time_sample_batch': 0.00042819874899827076, 'time_algorithm_update': 0.0068035466036132225, 'loss': 0.008132032031880457, 'time_step': 0.007887866907702571, 'init_value': -1.04853093624115, 'ave_value': -1.0482471513921092, 'soft_opc': nan} step=21992\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:11.14 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_21992.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f20d1b621ab54e8da040be012697b254",
       "version_major": 2,
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:14.30 [info     ] FQE_20220408020741: epoch=2 step=43984 epoch=2 metrics={'time_sample_batch': 0.0003912838730824215, 'time_algorithm_update': 0.006498671653445047, 'loss': 0.014115804965008364, 'time_step': 0.0074729861867085245, 'init_value': -1.1801666021347046, 'ave_value': -1.1798491936532454, 'soft_opc': nan} step=43984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:14.30 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_43984.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aa34bafd1b20486f8753429ff6824980",
       "version_major": 2,
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:18.00 [info     ] FQE_20220408020741: epoch=3 step=65976 epoch=3 metrics={'time_sample_batch': 0.0003805624306874086, 'time_algorithm_update': 0.007124566942962918, 'loss': 0.013537545082770205, 'time_step': 0.008145781149210258, 'init_value': -1.1299998760223389, 'ave_value': -1.1296853448110773, 'soft_opc': nan} step=65976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:18.00 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_65976.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ec83413147f42a3b8a65deaeacea44e",
       "version_major": 2,
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      ]
     },
     "metadata": {},
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    },
    {
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:21.44 [info     ] FQE_20220408020741: epoch=4 step=87968 epoch=4 metrics={'time_sample_batch': 0.0003992889676800031, 'time_algorithm_update': 0.007742372349246106, 'loss': 0.01322349580627041, 'time_step': 0.008771122661925698, 'init_value': -1.0982027053833008, 'ave_value': -1.0978867011420947, 'soft_opc': nan} step=87968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:21.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_87968.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92e8aef21ba8441d9313c80008480376",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:25.39 [info     ] FQE_20220408020741: epoch=5 step=109960 epoch=5 metrics={'time_sample_batch': 0.00040829423428796, 'time_algorithm_update': 0.008282270397694165, 'loss': 0.013019833495590686, 'time_step': 0.009316411949409577, 'init_value': -1.1884117126464844, 'ave_value': -1.1880607715054468, 'soft_opc': nan} step=109960\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:25.39 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_109960.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccd1006addc24174b2928f57c4e5dad5",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:29.44 [info     ] FQE_20220408020741: epoch=6 step=131952 epoch=6 metrics={'time_sample_batch': 0.00038001406361901487, 'time_algorithm_update': 0.008725947046245216, 'loss': 0.013688410214060035, 'time_step': 0.00972857539026726, 'init_value': -1.2011494636535645, 'ave_value': -1.200774246114679, 'soft_opc': nan} step=131952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 02:29.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_131952.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44aba342685d4ad79b2ceaf93414ce08",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:33.55 [info     ] FQE_20220408020741: epoch=7 step=153944 epoch=7 metrics={'time_sample_batch': 0.0003733601124341291, 'time_algorithm_update': 0.00908322284853905, 'loss': 0.013789376671307519, 'time_step': 0.010076942878360964, 'init_value': -1.178562045097351, 'ave_value': -1.1782537745086683, 'soft_opc': nan} step=153944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:33.55 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_153944.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3729fde38ef45ae964848369337e107",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:38.15 [info     ] FQE_20220408020741: epoch=8 step=175936 epoch=8 metrics={'time_sample_batch': 0.00037491706424559105, 'time_algorithm_update': 0.009411292071340734, 'loss': 0.0136731938448857, 'time_step': 0.01039413662770307, 'init_value': -1.1667624711990356, 'ave_value': -1.1663974353817537, 'soft_opc': nan} step=175936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:38.15 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_175936.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e74e5092dc4844e5828ca5ad9dbe23fb",
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     },
     "metadata": {},
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 02:42.39 [info     ] FQE_20220408020741: epoch=9 step=197928 epoch=9 metrics={'time_sample_batch': 0.00036654867185770967, 'time_algorithm_update': 0.009609924203137912, 'loss': 0.014461138973639837, 'time_step': 0.010621020829907414, 'init_value': -1.2460979223251343, 'ave_value': -1.2457539595397338, 'soft_opc': nan} step=197928\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:42.39 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_197928.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "134e4b70c2e34002ac88499237e061e6",
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     },
     "metadata": {},
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:47.06 [info     ] FQE_20220408020741: epoch=10 step=219920 epoch=10 metrics={'time_sample_batch': 0.00041369593503389325, 'time_algorithm_update': 0.009732476925147409, 'loss': 0.015195978153265, 'time_step': 0.010763975035801676, 'init_value': -1.178571105003357, 'ave_value': -1.178245537867333, 'soft_opc': nan} step=219920\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:47.06 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_219920.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e2d7e0ce2b2f4f49b7bcb9fcd739fa81",
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:51.36 [info     ] FQE_20220408020741: epoch=11 step=241912 epoch=11 metrics={'time_sample_batch': 0.00039768455331218853, 'time_algorithm_update': 0.009846720447450085, 'loss': 0.015017470780699748, 'time_step': 0.010861637788323672, 'init_value': -1.2587169408798218, 'ave_value': -1.2583700600108156, 'soft_opc': nan} step=241912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 02:51.36 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_241912.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "261001f23fc54c71a05c5988676407d2",
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 02:56.09 [info     ] FQE_20220408020741: epoch=12 step=263904 epoch=12 metrics={'time_sample_batch': 0.0003808344893561315, 'time_algorithm_update': 0.01001515837095572, 'loss': 0.01532685735135579, 'time_step': 0.01102011604454787, 'init_value': -1.2198436260223389, 'ave_value': -1.219451589602224, 'soft_opc': nan} step=263904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 02:56.09 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_263904.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f4203269cc12434e9b0cbb1770db3fe4",
       "version_major": 2,
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      },
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:00.44 [info     ] FQE_20220408020741: epoch=13 step=285896 epoch=13 metrics={'time_sample_batch': 0.00037179051984132704, 'time_algorithm_update': 0.0099900279989586, 'loss': 0.01541216225602033, 'time_step': 0.0110149803856322, 'init_value': -1.1952316761016846, 'ave_value': -1.1948308177486089, 'soft_opc': nan} step=285896\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:00.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_285896.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22b385f3b1ee4e239ff55707c3ceb745",
       "version_major": 2,
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:05.19 [info     ] FQE_20220408020741: epoch=14 step=307888 epoch=14 metrics={'time_sample_batch': 0.00037260026704532184, 'time_algorithm_update': 0.010066625865080782, 'loss': 0.014781611313542355, 'time_step': 0.011079221064066965, 'init_value': -1.1793956756591797, 'ave_value': -1.1790311668650468, 'soft_opc': nan} step=307888\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:05.19 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_307888.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14414abb14ab499cb8dda6b85202a308",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:09.55 [info     ] FQE_20220408020741: epoch=15 step=329880 epoch=15 metrics={'time_sample_batch': 0.00038117963906069937, 'time_algorithm_update': 0.010081261981830202, 'loss': 0.01491379159554399, 'time_step': 0.011130564448702505, 'init_value': -1.210632562637329, 'ave_value': -1.2102623421800829, 'soft_opc': nan} step=329880\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:09.55 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_329880.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6790aacd0f946c98321995cdd631663",
       "version_major": 2,
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:14.34 [info     ] FQE_20220408020741: epoch=16 step=351872 epoch=16 metrics={'time_sample_batch': 0.00037168652068575583, 'time_algorithm_update': 0.010274290496455665, 'loss': 0.013990870163828677, 'time_step': 0.011282890102741024, 'init_value': -1.1832112073898315, 'ave_value': -1.1828329455960132, 'soft_opc': nan} step=351872\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:14.34 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_351872.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65f68e568295414194d8cd807f5c58f1",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:19.15 [info     ] FQE_20220408020741: epoch=17 step=373864 epoch=17 metrics={'time_sample_batch': 0.0004039508683241771, 'time_algorithm_update': 0.01029291769598735, 'loss': 0.014820441949279261, 'time_step': 0.011352417403110463, 'init_value': -1.2133538722991943, 'ave_value': -1.2129884945605807, 'soft_opc': nan} step=373864\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:19.15 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_373864.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3550771ca08a4714a901952714a45ae9",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:23.56 [info     ] FQE_20220408020741: epoch=18 step=395856 epoch=18 metrics={'time_sample_batch': 0.00037348324622011654, 'time_algorithm_update': 0.01036846089163621, 'loss': 0.014370032767541384, 'time_step': 0.011376255444935687, 'init_value': -1.210204839706421, 'ave_value': -1.2098263550042436, 'soft_opc': nan} step=395856\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:23.56 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_395856.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3da61f8ac4f54f668a07784cdc745d54",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:28.42 [info     ] FQE_20220408020741: epoch=19 step=417848 epoch=19 metrics={'time_sample_batch': 0.00034444689447119176, 'time_algorithm_update': 0.010570102320796318, 'loss': 0.015354833488319928, 'time_step': 0.01156024469510561, 'init_value': -1.2272000312805176, 'ave_value': -1.2267880649539775, 'soft_opc': nan} step=417848\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:28.42 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_417848.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eff6d19fb05d485482929bb33db96474",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:33.26 [info     ] FQE_20220408020741: epoch=20 step=439840 epoch=20 metrics={'time_sample_batch': 0.00037611859978307763, 'time_algorithm_update': 0.010456425638882279, 'loss': 0.014971144081737576, 'time_step': 0.011496325738649449, 'init_value': -1.2011959552764893, 'ave_value': -1.2007792122675862, 'soft_opc': nan} step=439840\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 03:33.26 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_439840.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "49a9562cd50f46e5b23c4b11e835f127",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:38.10 [info     ] FQE_20220408020741: epoch=21 step=461832 epoch=21 metrics={'time_sample_batch': 0.000362673730319437, 'time_algorithm_update': 0.010470797470067796, 'loss': 0.01445489683584885, 'time_step': 0.011500338731396628, 'init_value': -1.2097176313400269, 'ave_value': -1.2093500900322451, 'soft_opc': nan} step=461832\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:38.10 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_461832.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba1c3979fd804298bb67392ce20cda79",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:42.56 [info     ] FQE_20220408020741: epoch=22 step=483824 epoch=22 metrics={'time_sample_batch': 0.00037080585067599155, 'time_algorithm_update': 0.010612215116354455, 'loss': 0.014485646328948209, 'time_step': 0.011614517250161641, 'init_value': -1.1990169286727905, 'ave_value': -1.1986303625573358, 'soft_opc': nan} step=483824\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:42.56 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_483824.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c43274cca5614abb88f0f765f5db417f",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:47.42 [info     ] FQE_20220408020741: epoch=23 step=505816 epoch=23 metrics={'time_sample_batch': 0.0003693660374943583, 'time_algorithm_update': 0.010543676355441036, 'loss': 0.015010832311105839, 'time_step': 0.011596446429308477, 'init_value': -1.1910401582717896, 'ave_value': -1.1906805763705626, 'soft_opc': nan} step=505816\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:47.42 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_505816.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "06d7484d7fec451c9cc73ff0527776f1",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:52.29 [info     ] FQE_20220408020741: epoch=24 step=527808 epoch=24 metrics={'time_sample_batch': 0.000401150176376809, 'time_algorithm_update': 0.01056322311218889, 'loss': 0.015260535837764221, 'time_step': 0.011627508384958534, 'init_value': -1.225817322731018, 'ave_value': -1.22545302492412, 'soft_opc': nan} step=527808\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:52.29 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_527808.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb7085777da645d8819bb7bf165a2ef4",
       "version_major": 2,
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     },
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:57.17 [info     ] FQE_20220408020741: epoch=25 step=549800 epoch=25 metrics={'time_sample_batch': 0.00039658948871255917, 'time_algorithm_update': 0.010631215251459127, 'loss': 0.015374435515485077, 'time_step': 0.011691832551092353, 'init_value': -1.2299308776855469, 'ave_value': -1.229568193714141, 'soft_opc': nan} step=549800\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 03:57.17 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_549800.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2330ea1edef345e9b996a019e7b04f0f",
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     "text": [
      "2022-04-08 04:02.03 [info     ] FQE_20220408020741: epoch=26 step=571792 epoch=26 metrics={'time_sample_batch': 0.0003771159747766555, 'time_algorithm_update': 0.010603303853361421, 'loss': 0.015582579221173024, 'time_step': 0.011642077113117206, 'init_value': -1.2246276140213013, 'ave_value': -1.224292888401025, 'soft_opc': nan} step=571792\n"
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     "text": [
      "2022-04-08 04:02.03 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_571792.pt\n"
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5acefe850225489381428f3503783a8e",
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 04:06.51 [info     ] FQE_20220408020741: epoch=27 step=593784 epoch=27 metrics={'time_sample_batch': 0.00033753688615554027, 'time_algorithm_update': 0.010620303048182072, 'loss': 0.014760690548940168, 'time_step': 0.011640487139161694, 'init_value': -1.1958141326904297, 'ave_value': -1.1954133982770005, 'soft_opc': nan} step=593784\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:06.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_593784.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e03c065ac6574cb495895a3795208d0c",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:11.40 [info     ] FQE_20220408020741: epoch=28 step=615776 epoch=28 metrics={'time_sample_batch': 0.00037192084131001474, 'time_algorithm_update': 0.010695207971102457, 'loss': 0.014666528312233626, 'time_step': 0.011714424012400273, 'init_value': -1.2758781909942627, 'ave_value': -1.2754921020932066, 'soft_opc': nan} step=615776\n"
     ]
    },
    {
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     "text": [
      "2022-04-08 04:11.40 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_615776.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a3f6e22fd0545f1a38c4d9b54a58c16",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:16.32 [info     ] FQE_20220408020741: epoch=29 step=637768 epoch=29 metrics={'time_sample_batch': 0.000386486056935965, 'time_algorithm_update': 0.010817405461137535, 'loss': 0.015728571843680976, 'time_step': 0.011855466478995819, 'init_value': -1.2528231143951416, 'ave_value': -1.2524333622146406, 'soft_opc': nan} step=637768\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:16.32 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_637768.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c37b4d50930d4dec9effb9ad9054447e",
       "version_major": 2,
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     "text": [
      "2022-04-08 04:21.23 [info     ] FQE_20220408020741: epoch=30 step=659760 epoch=30 metrics={'time_sample_batch': 0.00036934216528122706, 'time_algorithm_update': 0.010799367944298331, 'loss': 0.015370338631776656, 'time_step': 0.011835544271528092, 'init_value': -1.2034742832183838, 'ave_value': -1.2031440977194754, 'soft_opc': nan} step=659760\n"
     ]
    },
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     "text": [
      "2022-04-08 04:21.23 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_659760.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cbf5f9b1fb54b08ae8ae329bac44ddd",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:26.15 [info     ] FQE_20220408020741: epoch=31 step=681752 epoch=31 metrics={'time_sample_batch': 0.00037235504022378323, 'time_algorithm_update': 0.010838330702679337, 'loss': 0.014874389377415202, 'time_step': 0.011873345498216503, 'init_value': -1.1685596704483032, 'ave_value': -1.1682353009386857, 'soft_opc': nan} step=681752\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:26.15 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_681752.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f0e6b100e9144c89e8857a0f25d3673",
       "version_major": 2,
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     "metadata": {},
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     "text": [
      "2022-04-08 04:31.08 [info     ] FQE_20220408020741: epoch=32 step=703744 epoch=32 metrics={'time_sample_batch': 0.00037495883519799545, 'time_algorithm_update': 0.010863626759977678, 'loss': 0.014962267769872513, 'time_step': 0.011908592094547664, 'init_value': -1.2169644832611084, 'ave_value': -1.2166144019203726, 'soft_opc': nan} step=703744\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:31.08 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_703744.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e05ed9fc5d54296b87251b0f1307c4f",
       "version_major": 2,
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     "metadata": {},
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     "text": [
      "2022-04-08 04:36.04 [info     ] FQE_20220408020741: epoch=33 step=725736 epoch=33 metrics={'time_sample_batch': 0.0003953619018907155, 'time_algorithm_update': 0.01094903870902091, 'loss': 0.014115821971460052, 'time_step': 0.012015954456835844, 'init_value': -1.1948251724243164, 'ave_value': -1.1944614967724838, 'soft_opc': nan} step=725736\n"
     ]
    },
    {
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     "text": [
      "2022-04-08 04:36.04 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_725736.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "161f4c36364d49298397e1bf23255a03",
       "version_major": 2,
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      },
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     "metadata": {},
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     "text": [
      "2022-04-08 04:40.57 [info     ] FQE_20220408020741: epoch=34 step=747728 epoch=34 metrics={'time_sample_batch': 0.0003599792599634675, 'time_algorithm_update': 0.01089271014063694, 'loss': 0.014656958422562451, 'time_step': 0.011927463350056648, 'init_value': -1.2359524965286255, 'ave_value': -1.2355929341890513, 'soft_opc': nan} step=747728\n"
     ]
    },
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     "text": [
      "2022-04-08 04:40.57 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_747728.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "32114edd902248ce9b91651088c62e2f",
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     "text": [
      "2022-04-08 04:45.51 [info     ] FQE_20220408020741: epoch=35 step=769720 epoch=35 metrics={'time_sample_batch': 0.00037425245836284995, 'time_algorithm_update': 0.010925158375868063, 'loss': 0.014902296168892796, 'time_step': 0.011952934838850397, 'init_value': -1.2234947681427002, 'ave_value': -1.223165948154999, 'soft_opc': nan} step=769720\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:45.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_769720.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64ee6cd1aa9f4be3b9578930a328e782",
       "version_major": 2,
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:50.49 [info     ] FQE_20220408020741: epoch=36 step=791712 epoch=36 metrics={'time_sample_batch': 0.0003633109406151742, 'time_algorithm_update': 0.011088543965539831, 'loss': 0.015153863313089905, 'time_step': 0.01211970002705854, 'init_value': -1.204925298690796, 'ave_value': -1.2046061433469302, 'soft_opc': nan} step=791712\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:50.49 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_791712.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a79d9034aa6143908adaf51528522402",
       "version_major": 2,
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     "text": [
      "2022-04-08 04:55.46 [info     ] FQE_20220408020741: epoch=37 step=813704 epoch=37 metrics={'time_sample_batch': 0.00035709961191790024, 'time_algorithm_update': 0.01107164091004073, 'loss': 0.014489573930034432, 'time_step': 0.01209224741560366, 'init_value': -1.18284010887146, 'ave_value': -1.1825196913242322, 'soft_opc': nan} step=813704\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 04:55.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_813704.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ef3b341909fe45a692d7c75e38884d61",
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     "output_type": "stream",
     "text": [
      "2022-04-08 05:00.45 [info     ] FQE_20220408020741: epoch=38 step=835696 epoch=38 metrics={'time_sample_batch': 0.000378294071747026, 'time_algorithm_update': 0.011112139599242614, 'loss': 0.015089653002140853, 'time_step': 0.01217476581824654, 'init_value': -1.2071301937103271, 'ave_value': -1.2067873959206825, 'soft_opc': nan} step=835696\n"
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     "text": [
      "2022-04-08 05:00.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_835696.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96c2859271754594bf6a41f12eb59f2a",
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     "text": [
      "2022-04-08 05:05.44 [info     ] FQE_20220408020741: epoch=39 step=857688 epoch=39 metrics={'time_sample_batch': 0.0003885735963292016, 'time_algorithm_update': 0.011031333903218669, 'loss': 0.014244919717913558, 'time_step': 0.01211858222856657, 'init_value': -1.1966416835784912, 'ave_value': -1.1963128751755412, 'soft_opc': nan} step=857688\n"
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      "2022-04-08 05:05.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_857688.pt\n"
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "97601bdfba3e4f15ae20a79a0d133deb",
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     "text": [
      "2022-04-08 05:10.40 [info     ] FQE_20220408020741: epoch=40 step=879680 epoch=40 metrics={'time_sample_batch': 0.00036838843675906894, 'time_algorithm_update': 0.011047884632171653, 'loss': 0.014732958244264197, 'time_step': 0.012099765330585578, 'init_value': -1.2186814546585083, 'ave_value': -1.2183466649747783, 'soft_opc': nan} step=879680\n"
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     "text": [
      "2022-04-08 05:10.41 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_879680.pt\n"
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    },
    {
     "data": {
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       "model_id": "24f3e501e47e4f6db2e842f8f8c7e00e",
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     "text": [
      "2022-04-08 05:15.37 [info     ] FQE_20220408020741: epoch=41 step=901672 epoch=41 metrics={'time_sample_batch': 0.00035737521564279653, 'time_algorithm_update': 0.0110594197896317, 'loss': 0.015022458264846543, 'time_step': 0.012100014471062404, 'init_value': -1.22079336643219, 'ave_value': -1.2204404761286656, 'soft_opc': nan} step=901672\n"
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     "text": [
      "2022-04-08 05:15.37 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_901672.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c97dcd7fe92f4f82be063ee4c1108899",
       "version_major": 2,
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      },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 05:20.36 [info     ] FQE_20220408020741: epoch=42 step=923664 epoch=42 metrics={'time_sample_batch': 0.00037229521875600027, 'time_algorithm_update': 0.011103687762520277, 'loss': 0.015078046652036041, 'time_step': 0.012157892467499473, 'init_value': -1.2325043678283691, 'ave_value': -1.2321343272830656, 'soft_opc': nan} step=923664\n"
     ]
    },
    {
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     "text": [
      "2022-04-08 05:20.36 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_923664.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db20fe5dad894e8a95985968e1e6610f",
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     "output_type": "stream",
     "text": [
      "2022-04-08 05:25.35 [info     ] FQE_20220408020741: epoch=43 step=945656 epoch=43 metrics={'time_sample_batch': 0.0003887196049424855, 'time_algorithm_update': 0.011118797507447388, 'loss': 0.014665386250490839, 'time_step': 0.012184871916946128, 'init_value': -1.2339458465576172, 'ave_value': -1.233564530429282, 'soft_opc': nan} step=945656\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "2022-04-08 05:25.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_945656.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "516125732b8b4c6fa88ee410c7d65609",
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     "text": [
      "2022-04-08 05:30.35 [info     ] FQE_20220408020741: epoch=44 step=967648 epoch=44 metrics={'time_sample_batch': 0.00038685937194921354, 'time_algorithm_update': 0.011129354923237189, 'loss': 0.014882266819371602, 'time_step': 0.012193034858693205, 'init_value': -1.1961466073989868, 'ave_value': -1.1957734431899563, 'soft_opc': nan} step=967648\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "2022-04-08 05:30.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_967648.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "937aecb9c47a4085ae5a9d83d6846b1f",
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     "text": [
      "2022-04-08 05:35.34 [info     ] FQE_20220408020741: epoch=45 step=989640 epoch=45 metrics={'time_sample_batch': 0.00036163055146551425, 'time_algorithm_update': 0.011120324179925846, 'loss': 0.015404947236492526, 'time_step': 0.01216284221152732, 'init_value': -1.2860538959503174, 'ave_value': -1.2856972766640908, 'soft_opc': nan} step=989640\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 05:35.34 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_989640.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c736bfad131c41ce95ce312a5e1288c6",
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     "text": [
      "2022-04-08 05:40.34 [info     ] FQE_20220408020741: epoch=46 step=1011632 epoch=46 metrics={'time_sample_batch': 0.0003758481998103625, 'time_algorithm_update': 0.011143745325313302, 'loss': 0.01572526568750739, 'time_step': 0.012198358351380317, 'init_value': -1.2274831533432007, 'ave_value': -1.22713519798981, 'soft_opc': nan} step=1011632\n"
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    },
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     "text": [
      "2022-04-08 05:40.34 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1011632.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d80815b0a16453f91aae24e9ae51088",
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     "text": [
      "2022-04-08 05:45.35 [info     ] FQE_20220408020741: epoch=47 step=1033624 epoch=47 metrics={'time_sample_batch': 0.0003812438837178363, 'time_algorithm_update': 0.011158369972125623, 'loss': 0.015233504015251266, 'time_step': 0.01220292604242771, 'init_value': -1.176824688911438, 'ave_value': -1.1764513593733021, 'soft_opc': nan} step=1033624\n"
     ]
    },
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     "text": [
      "2022-04-08 05:45.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1033624.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a483ddce60524c4ca85a6e46299a66c3",
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     "text": [
      "2022-04-08 05:50.33 [info     ] FQE_20220408020741: epoch=48 step=1055616 epoch=48 metrics={'time_sample_batch': 0.0003803469953471073, 'time_algorithm_update': 0.011069998389029338, 'loss': 0.015464074498552924, 'time_step': 0.012139918935129798, 'init_value': -1.210030198097229, 'ave_value': -1.2096500636889773, 'soft_opc': nan} step=1055616\n"
     ]
    },
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     "text": [
      "2022-04-08 05:50.33 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1055616.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5135805ce8ff4f83a899c1163756b0a0",
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     "text": [
      "2022-04-08 05:55.31 [info     ] FQE_20220408020741: epoch=49 step=1077608 epoch=49 metrics={'time_sample_batch': 0.00040091836005828733, 'time_algorithm_update': 0.011055970417451494, 'loss': 0.015090523025290011, 'time_step': 0.012120722033145775, 'init_value': -1.2434805631637573, 'ave_value': -1.243125322819474, 'soft_opc': nan} step=1077608\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 05:55.31 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1077608.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38ef3f992aed4dfcbc460bd82abc8292",
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     "output_type": "stream",
     "text": [
      "2022-04-08 06:00.29 [info     ] FQE_20220408020741: epoch=50 step=1099600 epoch=50 metrics={'time_sample_batch': 0.0003804068601794918, 'time_algorithm_update': 0.011090717410208659, 'loss': 0.015253687829330974, 'time_step': 0.012175549037155562, 'init_value': -1.1979639530181885, 'ave_value': -1.197604612150099, 'soft_opc': nan} step=1099600\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 06:00.29 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1099600.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3608291d133f43f3a110cc1c5b8435da",
       "version_major": 2,
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     "output_type": "stream",
     "text": [
      "2022-04-08 06:05.28 [info     ] FQE_20220408020741: epoch=51 step=1121592 epoch=51 metrics={'time_sample_batch': 0.00038225510286044625, 'time_algorithm_update': 0.0111054302497793, 'loss': 0.015434161988080963, 'time_step': 0.01216373341913525, 'init_value': -1.2516844272613525, 'ave_value': -1.2512799943658721, 'soft_opc': nan} step=1121592\n"
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     "text": [
      "2022-04-08 06:05.28 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1121592.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1906943e23143858798463602c85f1c",
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     "text": [
      "2022-04-08 06:10.27 [info     ] FQE_20220408020741: epoch=52 step=1143584 epoch=52 metrics={'time_sample_batch': 0.000400972056276107, 'time_algorithm_update': 0.011062224416916655, 'loss': 0.015718872213818444, 'time_step': 0.012147063578463069, 'init_value': -1.2660133838653564, 'ave_value': -1.2656501578388526, 'soft_opc': nan} step=1143584\n"
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     "text": [
      "2022-04-08 06:10.27 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1143584.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dda7ba855f494167ae1d842e79219a7b",
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     "text": [
      "2022-04-08 06:15.28 [info     ] FQE_20220408020741: epoch=53 step=1165576 epoch=53 metrics={'time_sample_batch': 0.0004000874617699061, 'time_algorithm_update': 0.011158445816813663, 'loss': 0.015092585531598805, 'time_step': 0.012235234784576927, 'init_value': -1.226556420326233, 'ave_value': -1.2261494824172237, 'soft_opc': nan} step=1165576\n"
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     "text": [
      "2022-04-08 06:15.28 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1165576.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cdcd4831fd484b9fb326c8c54ae0cada",
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     "text": [
      "2022-04-08 06:20.31 [info     ] FQE_20220408020741: epoch=54 step=1187568 epoch=54 metrics={'time_sample_batch': 0.00038991216400745954, 'time_algorithm_update': 0.011298544626315753, 'loss': 0.015394252918662664, 'time_step': 0.012338677203711616, 'init_value': -1.2574529647827148, 'ave_value': -1.2570784960416648, 'soft_opc': nan} step=1187568\n"
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      "2022-04-08 06:20.31 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1187568.pt\n"
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    },
    {
     "data": {
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       "model_id": "a2969822b9d343cda725722792cf2885",
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     "text": [
      "2022-04-08 06:25.31 [info     ] FQE_20220408020741: epoch=55 step=1209560 epoch=55 metrics={'time_sample_batch': 0.0003808415144215761, 'time_algorithm_update': 0.011179693441644241, 'loss': 0.015287180064438004, 'time_step': 0.01226624125521935, 'init_value': -1.241339087486267, 'ave_value': -1.2408906423923458, 'soft_opc': nan} step=1209560\n"
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      "2022-04-08 06:25.31 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1209560.pt\n"
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    },
    {
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       "model_id": "f55f192a1922449891c3efa681c5deac",
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      "2022-04-08 06:30.34 [info     ] FQE_20220408020741: epoch=56 step=1231552 epoch=56 metrics={'time_sample_batch': 0.0003540992476628797, 'time_algorithm_update': 0.0112639947148313, 'loss': 0.015640155715730932, 'time_step': 0.012307887501870991, 'init_value': -1.2547167539596558, 'ave_value': -1.2543003251998697, 'soft_opc': nan} step=1231552\n"
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 06:30.34 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1231552.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9236b8286a945e78464fc44c70e543e",
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     "text": [
      "2022-04-08 06:35.35 [info     ] FQE_20220408020741: epoch=57 step=1253544 epoch=57 metrics={'time_sample_batch': 0.00039963673010181194, 'time_algorithm_update': 0.011201499277307753, 'loss': 0.014721849304912953, 'time_step': 0.012277001086127849, 'init_value': -1.2527986764907837, 'ave_value': -1.2523943080651578, 'soft_opc': nan} step=1253544\n"
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      "2022-04-08 06:35.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1253544.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2d5d05799e6740b0a48260aeb7dded28",
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     "text": [
      "2022-04-08 06:40.38 [info     ] FQE_20220408020741: epoch=58 step=1275536 epoch=58 metrics={'time_sample_batch': 0.00038421124751110777, 'time_algorithm_update': 0.011313517425831294, 'loss': 0.015020880323430089, 'time_step': 0.012363139771686635, 'init_value': -1.252206563949585, 'ave_value': -1.2518126764733541, 'soft_opc': nan} step=1275536\n"
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      "2022-04-08 06:40.38 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1275536.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a916a90264af45ecaaed58c649851e99",
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     "text": [
      "2022-04-08 06:45.40 [info     ] FQE_20220408020741: epoch=59 step=1297528 epoch=59 metrics={'time_sample_batch': 0.00037971737385663435, 'time_algorithm_update': 0.011266132177722024, 'loss': 0.015707575678057292, 'time_step': 0.01231384825905959, 'init_value': -1.200793981552124, 'ave_value': -1.2004006607905935, 'soft_opc': nan} step=1297528\n"
     ]
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      "2022-04-08 06:45.40 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1297528.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d174563657e642e1882bbb5f7c8b04c9",
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     "text": [
      "2022-04-08 06:50.44 [info     ] FQE_20220408020741: epoch=60 step=1319520 epoch=60 metrics={'time_sample_batch': 0.0003676148556327334, 'time_algorithm_update': 0.011327519064254872, 'loss': 0.015393708785364398, 'time_step': 0.012380888632055888, 'init_value': -1.2482068538665771, 'ave_value': -1.2478018929787216, 'soft_opc': nan} step=1319520\n"
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      "2022-04-08 06:50.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1319520.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1e0ec3478cf4f2d857036af7e26ae2b",
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     "text": [
      "2022-04-08 06:55.46 [info     ] FQE_20220408020741: epoch=61 step=1341512 epoch=61 metrics={'time_sample_batch': 0.00036539021905182317, 'time_algorithm_update': 0.011258206559597861, 'loss': 0.015702759774323706, 'time_step': 0.012293179313153864, 'init_value': -1.1933130025863647, 'ave_value': -1.192937039853483, 'soft_opc': nan} step=1341512\n"
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      "2022-04-08 06:55.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1341512.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "835f1284688e4d0f86dc7e8ab751ee8f",
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     "text": [
      "2022-04-08 07:00.48 [info     ] FQE_20220408020741: epoch=62 step=1363504 epoch=62 metrics={'time_sample_batch': 0.00036152730034931914, 'time_algorithm_update': 0.011220758765252905, 'loss': 0.015166705884946808, 'time_step': 0.01231420866226274, 'init_value': -1.2310155630111694, 'ave_value': -1.2306085638542428, 'soft_opc': nan} step=1363504\n"
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      "2022-04-08 07:00.48 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1363504.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ea954bbd45914502b69454ec1af35f30",
       "version_major": 2,
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     "text": [
      "2022-04-08 07:05.51 [info     ] FQE_20220408020741: epoch=63 step=1385496 epoch=63 metrics={'time_sample_batch': 0.00037649488527152964, 'time_algorithm_update': 0.011274356642997495, 'loss': 0.01536635693766374, 'time_step': 0.01235373732869345, 'init_value': -1.2344346046447754, 'ave_value': -1.2341094624894373, 'soft_opc': nan} step=1385496\n"
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      "2022-04-08 07:05.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1385496.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "76fce6e3ab9942b48eb305376ef04c62",
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     "text": [
      "2022-04-08 07:10.54 [info     ] FQE_20220408020741: epoch=64 step=1407488 epoch=64 metrics={'time_sample_batch': 0.00039758422930659534, 'time_algorithm_update': 0.011205706706086954, 'loss': 0.01567520074321924, 'time_step': 0.012339715666665125, 'init_value': -1.3090342283248901, 'ave_value': -1.308624639998373, 'soft_opc': nan} step=1407488\n"
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     "text": [
      "2022-04-08 07:10.54 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1407488.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54b9fbe3386a4aff959790bcbd32a1c9",
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     "text": [
      "2022-04-08 07:15.57 [info     ] FQE_20220408020741: epoch=65 step=1429480 epoch=65 metrics={'time_sample_batch': 0.000363985531197413, 'time_algorithm_update': 0.011334664965161587, 'loss': 0.015930469536637938, 'time_step': 0.012387951034422397, 'init_value': -1.2547879219055176, 'ave_value': -1.254349686460485, 'soft_opc': nan} step=1429480\n"
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     "text": [
      "2022-04-08 07:15.57 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1429480.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afb6d3d0ab6d402e9d5b8a408610add8",
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     "text": [
      "2022-04-08 07:21.02 [info     ] FQE_20220408020741: epoch=66 step=1451472 epoch=66 metrics={'time_sample_batch': 0.0003680682542238204, 'time_algorithm_update': 0.011352313805077457, 'loss': 0.0153836225249029, 'time_step': 0.012430430434321698, 'init_value': -1.2293874025344849, 'ave_value': -1.2289809730234604, 'soft_opc': nan} step=1451472\n"
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     "text": [
      "2022-04-08 07:21.02 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1451472.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "710c2772fe31421c8930098bb1bce15e",
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     "text": [
      "2022-04-08 07:26.08 [info     ] FQE_20220408020741: epoch=67 step=1473464 epoch=67 metrics={'time_sample_batch': 0.00035872229362410085, 'time_algorithm_update': 0.011405204763929382, 'loss': 0.015187628309647292, 'time_step': 0.012465147126063558, 'init_value': -1.262821078300476, 'ave_value': -1.2623595373613952, 'soft_opc': nan} step=1473464\n"
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     "text": [
      "2022-04-08 07:26.08 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1473464.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "cb44924ee03d4ac5bcb1930d025e55f4",
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     "text": [
      "2022-04-08 07:31.14 [info     ] FQE_20220408020741: epoch=68 step=1495456 epoch=68 metrics={'time_sample_batch': 0.00037140299207993356, 'time_algorithm_update': 0.011392967219177529, 'loss': 0.015733029843901607, 'time_step': 0.012452682730240057, 'init_value': -1.2402030229568481, 'ave_value': -1.2397761040667397, 'soft_opc': nan} step=1495456\n"
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      "2022-04-08 07:31.14 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1495456.pt\n"
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    },
    {
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     "text": [
      "2022-04-08 07:36.20 [info     ] FQE_20220408020741: epoch=69 step=1517448 epoch=69 metrics={'time_sample_batch': 0.00038554395012701154, 'time_algorithm_update': 0.011383248995156147, 'loss': 0.0159493865300328, 'time_step': 0.012464945187955477, 'init_value': -1.238614559173584, 'ave_value': -1.2381722832477693, 'soft_opc': nan} step=1517448\n"
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     "text": [
      "2022-04-08 07:36.20 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1517448.pt\n"
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    {
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      "2022-04-08 07:41.25 [info     ] FQE_20220408020741: epoch=70 step=1539440 epoch=70 metrics={'time_sample_batch': 0.0003706364143367419, 'time_algorithm_update': 0.01136309153472523, 'loss': 0.015429273580581755, 'time_step': 0.01243588752250491, 'init_value': -1.245474100112915, 'ave_value': -1.2450367359219856, 'soft_opc': nan} step=1539440\n"
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      "2022-04-08 07:41.25 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1539440.pt\n"
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    {
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     "text": [
      "2022-04-08 07:46.28 [info     ] FQE_20220408020741: epoch=71 step=1561432 epoch=71 metrics={'time_sample_batch': 0.0003991188700305804, 'time_algorithm_update': 0.011333500430470338, 'loss': 0.0160042561664411, 'time_step': 0.012399060232242961, 'init_value': -1.2415640354156494, 'ave_value': -1.241161275780358, 'soft_opc': nan} step=1561432\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 07:46.28 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1561432.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "363c38847c8e44af8782b9da4ead3196",
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     "output_type": "stream",
     "text": [
      "2022-04-08 07:51.34 [info     ] FQE_20220408020741: epoch=72 step=1583424 epoch=72 metrics={'time_sample_batch': 0.00037620827777900016, 'time_algorithm_update': 0.011364132545349077, 'loss': 0.016178640332120315, 'time_step': 0.012458005604981595, 'init_value': -1.2826638221740723, 'ave_value': -1.2822556356711927, 'soft_opc': nan} step=1583424\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 07:51.34 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1583424.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72feb0bcee11430c872092e0570d9791",
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     "output_type": "stream",
     "text": [
      "2022-04-08 07:56.39 [info     ] FQE_20220408020741: epoch=73 step=1605416 epoch=73 metrics={'time_sample_batch': 0.0003766301286224887, 'time_algorithm_update': 0.011367395807500751, 'loss': 0.01590811633327822, 'time_step': 0.012434604786751097, 'init_value': -1.2584995031356812, 'ave_value': -1.2580423769022058, 'soft_opc': nan} step=1605416\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 07:56.39 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1605416.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e1db6177720444caa806c14668dc38ad",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:01.42 [info     ] FQE_20220408020741: epoch=74 step=1627408 epoch=74 metrics={'time_sample_batch': 0.0003699047450977657, 'time_algorithm_update': 0.011277846767061969, 'loss': 0.01593246366192407, 'time_step': 0.012356988318662897, 'init_value': -1.3130182027816772, 'ave_value': -1.3125903135421395, 'soft_opc': nan} step=1627408\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "2022-04-08 08:01.42 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1627408.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "27a253d92eaf47cea1ec4dd3bb6c43d5",
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     "text": [
      "2022-04-08 08:06.44 [info     ] FQE_20220408020741: epoch=75 step=1649400 epoch=75 metrics={'time_sample_batch': 0.0003860684124588229, 'time_algorithm_update': 0.011209532721513572, 'loss': 0.01572084084111743, 'time_step': 0.01228419109967545, 'init_value': -1.2322534322738647, 'ave_value': -1.2317938280528882, 'soft_opc': nan} step=1649400\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:06.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1649400.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9eb83553b1e249409c3fadcb266a514a",
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     "text": [
      "2022-04-08 08:11.45 [info     ] FQE_20220408020741: epoch=76 step=1671392 epoch=76 metrics={'time_sample_batch': 0.0003647723818918105, 'time_algorithm_update': 0.01120554806753348, 'loss': 0.016316065878336814, 'time_step': 0.012274595738411297, 'init_value': -1.2567884922027588, 'ave_value': -1.2564129209234245, 'soft_opc': nan} step=1671392\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:11.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1671392.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12e27b2b44f9410d913671806032f9be",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:16.45 [info     ] FQE_20220408020741: epoch=77 step=1693384 epoch=77 metrics={'time_sample_batch': 0.00036357426131669287, 'time_algorithm_update': 0.01118790870278304, 'loss': 0.01623463530772598, 'time_step': 0.01223290916268025, 'init_value': -1.2873188257217407, 'ave_value': -1.2869476147637031, 'soft_opc': nan} step=1693384\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:16.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1693384.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b50e1e3839d4216b9542bec4b86c09d",
       "version_major": 2,
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      },
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     "text": [
      "2022-04-08 08:21.46 [info     ] FQE_20220408020741: epoch=78 step=1715376 epoch=78 metrics={'time_sample_batch': 0.00038513539053388577, 'time_algorithm_update': 0.011195831686507663, 'loss': 0.01633410734856297, 'time_step': 0.012256829705659847, 'init_value': -1.2125732898712158, 'ave_value': -1.2121865979504003, 'soft_opc': nan} step=1715376\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:21.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1715376.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "892148716e884cd5bf9ca973c108de83",
       "version_major": 2,
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     },
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     "text": [
      "2022-04-08 08:26.47 [info     ] FQE_20220408020741: epoch=79 step=1737368 epoch=79 metrics={'time_sample_batch': 0.0003841118124798455, 'time_algorithm_update': 0.011187373746217663, 'loss': 0.015697963395060147, 'time_step': 0.012258801288108646, 'init_value': -1.270081639289856, 'ave_value': -1.2696761126108886, 'soft_opc': nan} step=1737368\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:26.47 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1737368.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cf68dbe4e90b460db6a19de0b719adc9",
       "version_major": 2,
       "version_minor": 0
      },
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:31.50 [info     ] FQE_20220408020741: epoch=80 step=1759360 epoch=80 metrics={'time_sample_batch': 0.00037776042696084487, 'time_algorithm_update': 0.011220632324916054, 'loss': 0.016499009055236668, 'time_step': 0.01231038836012237, 'init_value': -1.2602238655090332, 'ave_value': -1.2597597510954082, 'soft_opc': nan} step=1759360\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:31.50 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1759360.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "44f133679f4e46c89d8659edf6597231",
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     "metadata": {},
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     "text": [
      "2022-04-08 08:36.56 [info     ] FQE_20220408020741: epoch=81 step=1781352 epoch=81 metrics={'time_sample_batch': 0.00038913110248736356, 'time_algorithm_update': 0.011320270877094345, 'loss': 0.016302833042298914, 'time_step': 0.012411280301060404, 'init_value': -1.2690223455429077, 'ave_value': -1.268485720436508, 'soft_opc': nan} step=1781352\n"
     ]
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     "output_type": "stream",
     "text": [
      "2022-04-08 08:36.56 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1781352.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16fadc7e9673438dbaf195823adcb25a",
       "version_major": 2,
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     "text": [
      "2022-04-08 08:42.03 [info     ] FQE_20220408020741: epoch=82 step=1803344 epoch=82 metrics={'time_sample_batch': 0.00041254366168372194, 'time_algorithm_update': 0.011421029092442473, 'loss': 0.016619189123278623, 'time_step': 0.012518011621667411, 'init_value': -1.247281789779663, 'ave_value': -1.246725756072301, 'soft_opc': nan} step=1803344\n"
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     "text": [
      "2022-04-08 08:42.03 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1803344.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7a6241faf7c4ec59a098a88231e9a66",
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     "text": [
      "2022-04-08 08:47.10 [info     ] FQE_20220408020741: epoch=83 step=1825336 epoch=83 metrics={'time_sample_batch': 0.00040798824281855554, 'time_algorithm_update': 0.011422617971441796, 'loss': 0.01642304026996507, 'time_step': 0.012507636142063246, 'init_value': -1.2681570053100586, 'ave_value': -1.267602028056938, 'soft_opc': nan} step=1825336\n"
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      "2022-04-08 08:47.10 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1825336.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "fe13cadf1143442e8c913c486ac4da60",
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     "text": [
      "2022-04-08 08:52.18 [info     ] FQE_20220408020741: epoch=84 step=1847328 epoch=84 metrics={'time_sample_batch': 0.00038765127461968705, 'time_algorithm_update': 0.011505835194638011, 'loss': 0.01716360852780485, 'time_step': 0.012580761772019076, 'init_value': -1.2659777402877808, 'ave_value': -1.26550164105118, 'soft_opc': nan} step=1847328\n"
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      "2022-04-08 08:52.18 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1847328.pt\n"
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    },
    {
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       "model_id": "1db4e421616b4f27b32d04152a76f3a9",
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     "text": [
      "2022-04-08 08:57.26 [info     ] FQE_20220408020741: epoch=85 step=1869320 epoch=85 metrics={'time_sample_batch': 0.0003904464375803626, 'time_algorithm_update': 0.011474361589666972, 'loss': 0.017145835661249123, 'time_step': 0.012533590734200115, 'init_value': -1.2997736930847168, 'ave_value': -1.2992603812986963, 'soft_opc': nan} step=1869320\n"
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     "text": [
      "2022-04-08 08:57.26 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1869320.pt\n"
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    {
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     },
     "metadata": {},
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:02.34 [info     ] FQE_20220408020741: epoch=86 step=1891312 epoch=86 metrics={'time_sample_batch': 0.00041339008449944376, 'time_algorithm_update': 0.01147538222976926, 'loss': 0.01723085610809842, 'time_step': 0.012569676674075802, 'init_value': -1.2896400690078735, 'ave_value': -1.289163794996955, 'soft_opc': nan} step=1891312\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:02.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1891312.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b253e2ced58d49b09747c7807fcfd16d",
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     "text": [
      "2022-04-08 09:07.43 [info     ] FQE_20220408020741: epoch=87 step=1913304 epoch=87 metrics={'time_sample_batch': 0.0003896845323729836, 'time_algorithm_update': 0.011469201071993488, 'loss': 0.016645839366646493, 'time_step': 0.012558177595964212, 'init_value': -1.2726812362670898, 'ave_value': -1.2721532845435082, 'soft_opc': nan} step=1913304\n"
     ]
    },
    {
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     "output_type": "stream",
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     "text": [
      "2022-04-08 09:07.43 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1913304.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a86ce4e0c73e443caf3e9cb1cfc88684",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:12.51 [info     ] FQE_20220408020741: epoch=88 step=1935296 epoch=88 metrics={'time_sample_batch': 0.00038322298992499735, 'time_algorithm_update': 0.011456005158707115, 'loss': 0.017013987913170568, 'time_step': 0.012551283003764397, 'init_value': -1.3399386405944824, 'ave_value': -1.3393718315134622, 'soft_opc': nan} step=1935296\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:12.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1935296.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9cfb1cdc85484c4486c8f6c99e2e08ed",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:17.59 [info     ] FQE_20220408020741: epoch=89 step=1957288 epoch=89 metrics={'time_sample_batch': 0.00038253033798622973, 'time_algorithm_update': 0.011511620108367487, 'loss': 0.01757732795776835, 'time_step': 0.01259404647432704, 'init_value': -1.2922395467758179, 'ave_value': -1.2916966213551797, 'soft_opc': nan} step=1957288\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:17.59 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1957288.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "685586bcc16e4f07b5b6759b35369932",
       "version_major": 2,
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     "text": [
      "2022-04-08 09:23.07 [info     ] FQE_20220408020741: epoch=90 step=1979280 epoch=90 metrics={'time_sample_batch': 0.00038444350831679495, 'time_algorithm_update': 0.011479272197141956, 'loss': 0.01744351320156623, 'time_step': 0.012556438838060919, 'init_value': -1.3152589797973633, 'ave_value': -1.314684018944249, 'soft_opc': nan} step=1979280\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:23.07 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_1979280.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8243db20aa734c3aac8922df569e798f",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:28.16 [info     ] FQE_20220408020741: epoch=91 step=2001272 epoch=91 metrics={'time_sample_batch': 0.00041059558284894124, 'time_algorithm_update': 0.011477566049419, 'loss': 0.018381834194248825, 'time_step': 0.012569761538600956, 'init_value': -1.3281701803207397, 'ave_value': -1.3276306936603273, 'soft_opc': nan} step=2001272\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:28.16 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2001272.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "71f3e35a036f44478206b86bd458f186",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:33.26 [info     ] FQE_20220408020741: epoch=92 step=2023264 epoch=92 metrics={'time_sample_batch': 0.0004160908427226322, 'time_algorithm_update': 0.011498908675250333, 'loss': 0.017779817829800978, 'time_step': 0.012613473197857914, 'init_value': -1.296536922454834, 'ave_value': -1.2959997917112427, 'soft_opc': nan} step=2023264\n"
     ]
    },
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     "text": [
      "2022-04-08 09:33.26 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2023264.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4924bc44258c4b2a9ce70972c5ad1b18",
       "version_major": 2,
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     "metadata": {},
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     "text": [
      "2022-04-08 09:38.35 [info     ] FQE_20220408020741: epoch=93 step=2045256 epoch=93 metrics={'time_sample_batch': 0.00039280368311269017, 'time_algorithm_update': 0.01151518400646028, 'loss': 0.017527944546325076, 'time_step': 0.012626483207097614, 'init_value': -1.2722762823104858, 'ave_value': -1.271701763752853, 'soft_opc': nan} step=2045256\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:38.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2045256.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "714bee91663f4df1bb74dba6c8cb93bf",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:43.43 [info     ] FQE_20220408020741: epoch=94 step=2067248 epoch=94 metrics={'time_sample_batch': 0.000389753471248234, 'time_algorithm_update': 0.011507501186739969, 'loss': 0.017462812902458998, 'time_step': 0.012605286915114075, 'init_value': -1.303606390953064, 'ave_value': -1.3030695321273307, 'soft_opc': nan} step=2067248\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:43.43 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2067248.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e8db4add19b44259e1a1372312c7bd4",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:48.53 [info     ] FQE_20220408020741: epoch=95 step=2089240 epoch=95 metrics={'time_sample_batch': 0.0003812483502717918, 'time_algorithm_update': 0.01155491004687476, 'loss': 0.017388913211297003, 'time_step': 0.012642123756429506, 'init_value': -1.2739723920822144, 'ave_value': -1.2734072897106885, 'soft_opc': nan} step=2089240\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:48.53 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2089240.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8f3e5ad562b8483ebc17384fccf60f1a",
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:54.01 [info     ] FQE_20220408020741: epoch=96 step=2111232 epoch=96 metrics={'time_sample_batch': 0.00039025956867130596, 'time_algorithm_update': 0.01143830764202605, 'loss': 0.017151054956247462, 'time_step': 0.01256078040439374, 'init_value': -1.2795838117599487, 'ave_value': -1.2790059942711485, 'soft_opc': nan} step=2111232\n"
     ]
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:54.01 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2111232.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53108d4ca5424d7cb500ce768e036f61",
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     "text": [
      "2022-04-08 09:59.09 [info     ] FQE_20220408020741: epoch=97 step=2133224 epoch=97 metrics={'time_sample_batch': 0.00039094161796500443, 'time_algorithm_update': 0.011482960540799931, 'loss': 0.01679308502885544, 'time_step': 0.012596381647277199, 'init_value': -1.2661212682724, 'ave_value': -1.2655365930553728, 'soft_opc': nan} step=2133224\n"
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     "output_type": "stream",
     "text": [
      "2022-04-08 09:59.09 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2133224.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "db4f1d9965014ab3bbfa2788181c9857",
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     "text": [
      "2022-04-08 10:04.18 [info     ] FQE_20220408020741: epoch=98 step=2155216 epoch=98 metrics={'time_sample_batch': 0.00037866464394922903, 'time_algorithm_update': 0.011534214843702819, 'loss': 0.016933004028431387, 'time_step': 0.01262052578898333, 'init_value': -1.2810641527175903, 'ave_value': -1.2804994421078, 'soft_opc': nan} step=2155216\n"
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:04.18 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2155216.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dd6ca606c0e46529c8ea6a3a9d3b2a2",
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     "text": [
      "2022-04-08 10:09.27 [info     ] FQE_20220408020741: epoch=99 step=2177208 epoch=99 metrics={'time_sample_batch': 0.000393312534187958, 'time_algorithm_update': 0.011495847383730192, 'loss': 0.018008679714255023, 'time_step': 0.01260465952690058, 'init_value': -1.2670302391052246, 'ave_value': -1.2664396247129823, 'soft_opc': nan} step=2177208\n"
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     "output_type": "stream",
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      "2022-04-08 10:09.27 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2177208.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0575618f79a4f6ea9c48a40fa329131",
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     "text": [
      "2022-04-08 10:14.37 [info     ] FQE_20220408020741: epoch=100 step=2199200 epoch=100 metrics={'time_sample_batch': 0.0003767586721425145, 'time_algorithm_update': 0.011553301285205646, 'loss': 0.01729562495076955, 'time_step': 0.01263644972598609, 'init_value': -1.2745429277420044, 'ave_value': -1.2739250281759056, 'soft_opc': nan} step=2199200\n"
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:14.37 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408020741\\model_2199200.pt\n"
     ]
    },
    {
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       " (98,\n",
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       " (99,\n",
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       "   'soft_opc': nan}),\n",
       " (100,\n",
       "  {'time_sample_batch': 0.0003767586721425145,\n",
       "   'time_algorithm_update': 0.011553301285205646,\n",
       "   'loss': 0.01729562495076955,\n",
       "   'time_step': 0.01263644972598609,\n",
       "   'init_value': -1.2745429277420044,\n",
       "   'ave_value': -1.2739250281759056,\n",
       "   'soft_opc': nan})]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from d3rlpy.ope import FQE\n",
    "# metrics to evaluate with\n",
    "from d3rlpy.metrics.scorer import soft_opc_scorer\n",
    "\n",
    "\n",
    "ope_dataset = get_dataset([i*2 for i in range(100)], path=\"collected_data/rl_deterministic.txt\") #change if you'd prefer different chunks\n",
    "ope_train_episodes, ope_test_episodes = train_test_split(ope_dataset, test_size=0.2)\n",
    "\n",
    "fqe = FQE(algo=model, action_scaler = action_scaler, use_gpu=False) #change this if you have one!\n",
    "fqe.fit(ope_train_episodes, eval_episodes=ope_test_episodes,\n",
    "        tensorboard_dir='runs',\n",
    "        n_epochs=100, n_steps_per_epoch=10000, #change if overfitting/underfitting\n",
    "        scorers={\n",
    "           'init_value': initial_state_value_estimation_scorer,\n",
    "            'ave_value': average_value_estimation_scorer,\n",
    "           'soft_opc': soft_opc_scorer(return_threshold=0)\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5d639afd-476b-4844-9921-f001f81abf34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "torch.Size([2777000, 6])\n",
      "2022-04-08 10:15.31 [debug    ] RoundIterator is selected.\n",
      "2022-04-08 10:15.31 [info     ] Directory is created at d3rlpy_logs\\FQE_20220408101531\n",
      "2022-04-08 10:15.31 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-08 10:15.31 [debug    ] Building models...\n",
      "2022-04-08 10:15.31 [debug    ] Models have been built.\n",
      "2022-04-08 10:15.31 [info     ] Parameters are saved to d3rlpy_logs\\FQE_20220408101531\\params.json params={'action_scaler': {'type': 'min_max', 'params': {'minimum': array(-0.6), 'maximum': array(0.6)}}, 'batch_size': 100, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 0.0001, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'bootstrap': False, 'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': None, 'target_update_interval': 100, 'use_gpu': None, 'algorithm': 'FQE', 'observation_shape': (6,), 'action_size': 3}\n"
     ]
    },
    {
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       "model_id": "c13c34b40df141279a313803f38c1ace",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aero\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\numpy\\core\\fromnumeric.py:3474: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "C:\\Users\\Aero\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.10_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python310\\site-packages\\numpy\\core\\_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:18.44 [info     ] FQE_20220408101531: epoch=1 step=21992 epoch=1 metrics={'time_sample_batch': 0.0004048699593708792, 'time_algorithm_update': 0.006098231745876369, 'loss': 0.008039890150641332, 'time_step': 0.007181064936844988, 'init_value': -1.154395341873169, 'ave_value': -1.1544075110241856, 'soft_opc': nan} step=21992\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:18.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_21992.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3a7bb91389e74e38b7b528b9d4affc1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:21.56 [info     ] FQE_20220408101531: epoch=2 step=43984 epoch=2 metrics={'time_sample_batch': 0.00037823652692082235, 'time_algorithm_update': 0.006257258098140202, 'loss': 0.013434912411280527, 'time_step': 0.007295431571251352, 'init_value': -1.180904746055603, 'ave_value': -1.1811056208214301, 'soft_opc': nan} step=43984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:21.56 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_43984.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f832485f0d94a75858f15b99f766087",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:25.22 [info     ] FQE_20220408101531: epoch=3 step=65976 epoch=3 metrics={'time_sample_batch': 0.00041217648276158705, 'time_algorithm_update': 0.006883125384966648, 'loss': 0.014252102441193727, 'time_step': 0.007972539425243157, 'init_value': -1.1604664325714111, 'ave_value': -1.1606499106798156, 'soft_opc': nan} step=65976\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:25.22 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_65976.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ba397f8e2e2b4bdd8bd9c6254fb59464",
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      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:29.00 [info     ] FQE_20220408101531: epoch=4 step=87968 epoch=4 metrics={'time_sample_batch': 0.000401743122255555, 'time_algorithm_update': 0.007437083332180413, 'loss': 0.015185871014634358, 'time_step': 0.008545193673481114, 'init_value': -1.195298671722412, 'ave_value': -1.1952821628598946, 'soft_opc': nan} step=87968\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:29.00 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_87968.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e394448dad99484ab0cef17e28c8733a",
       "version_major": 2,
       "version_minor": 0
      },
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:32.50 [info     ] FQE_20220408101531: epoch=5 step=109960 epoch=5 metrics={'time_sample_batch': 0.0003641567238030238, 'time_algorithm_update': 0.008003419498913329, 'loss': 0.015308996614840115, 'time_step': 0.009047474599656472, 'init_value': -1.169339895248413, 'ave_value': -1.1693944027291105, 'soft_opc': nan} step=109960\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:32.50 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_109960.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6a48a7b4efa4472a9954747f333ebe6e",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:36.48 [info     ] FQE_20220408101531: epoch=6 step=131952 epoch=6 metrics={'time_sample_batch': 0.00035926935975444927, 'time_algorithm_update': 0.008394934667939054, 'loss': 0.014609038872071893, 'time_step': 0.009419298922204677, 'init_value': -1.138919472694397, 'ave_value': -1.138992706594444, 'soft_opc': nan} step=131952\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:36.48 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_131952.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b672785fbad445cae1af23e8191d08d",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:40.53 [info     ] FQE_20220408101531: epoch=7 step=153944 epoch=7 metrics={'time_sample_batch': 0.0003800832843641751, 'time_algorithm_update': 0.008689667234077329, 'loss': 0.014428027205826493, 'time_step': 0.009751578240708552, 'init_value': -1.1590601205825806, 'ave_value': -1.1591620210023603, 'soft_opc': nan} step=153944\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:40.53 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_153944.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e71fc24f9c7f41f0ac48504ed288a949",
       "version_major": 2,
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:45.02 [info     ] FQE_20220408101531: epoch=8 step=175936 epoch=8 metrics={'time_sample_batch': 0.0003831527392705513, 'time_algorithm_update': 0.008864249184418697, 'loss': 0.01323338779321702, 'time_step': 0.009893151934513745, 'init_value': -1.1980708837509155, 'ave_value': -1.1981059106975052, 'soft_opc': nan} step=175936\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:45.02 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_175936.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff69b5c4f885402b87062db481993fcb",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:49.12 [info     ] FQE_20220408101531: epoch=9 step=197928 epoch=9 metrics={'time_sample_batch': 0.0004104103509535916, 'time_algorithm_update': 0.008907498143377544, 'loss': 0.014349795552355077, 'time_step': 0.009981999866916466, 'init_value': -1.1312906742095947, 'ave_value': -1.1312130914176615, 'soft_opc': nan} step=197928\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 10:49.12 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_197928.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bd6a607eb2cf45b3b47f2e3f96bac1e4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:53.26 [info     ] FQE_20220408101531: epoch=10 step=219920 epoch=10 metrics={'time_sample_batch': 0.00037146658626804787, 'time_algorithm_update': 0.009101748000322233, 'loss': 0.014582610868446762, 'time_step': 0.010134937167818133, 'init_value': -1.2426053285598755, 'ave_value': -1.2426685256248935, 'soft_opc': nan} step=219920\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:53.26 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_219920.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "adc5a43c5a024cb7bad12cd6999b270e",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    },
    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:57.42 [info     ] FQE_20220408101531: epoch=11 step=241912 epoch=11 metrics={'time_sample_batch': 0.0003536223454577743, 'time_algorithm_update': 0.009227238056138455, 'loss': 0.014690146030782364, 'time_step': 0.010244167471157502, 'init_value': -1.1994329690933228, 'ave_value': -1.1994973462173737, 'soft_opc': nan} step=241912\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 10:57.42 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_241912.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59d7c67176d54fbe839aacf20dd23909",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:02.02 [info     ] FQE_20220408101531: epoch=12 step=263904 epoch=12 metrics={'time_sample_batch': 0.00039331907140163563, 'time_algorithm_update': 0.009351231339368963, 'loss': 0.013931506289044257, 'time_step': 0.010394558034059394, 'init_value': -1.215981125831604, 'ave_value': -1.216004924007886, 'soft_opc': nan} step=263904\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 11:02.02 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_263904.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "220e956633ab4a3e9479ccf65e7fe62c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
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    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:06.24 [info     ] FQE_20220408101531: epoch=13 step=285896 epoch=13 metrics={'time_sample_batch': 0.0003573706732007884, 'time_algorithm_update': 0.009499633776226578, 'loss': 0.015166496552072908, 'time_step': 0.010549570981612333, 'init_value': -1.227781057357788, 'ave_value': -1.2278013146218947, 'soft_opc': nan} step=285896\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:06.24 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_285896.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "13c175afdedd48529b45310168588114",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:10.51 [info     ] FQE_20220408101531: epoch=14 step=307888 epoch=14 metrics={'time_sample_batch': 0.0003863673463393316, 'time_algorithm_update': 0.009680380384112845, 'loss': 0.014558229442230909, 'time_step': 0.010715630129060991, 'init_value': -1.246973991394043, 'ave_value': -1.2470047649558438, 'soft_opc': nan} step=307888\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:10.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_307888.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d9e551e60d8249f7a636b94cb8dd3bfe",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:15.20 [info     ] FQE_20220408101531: epoch=15 step=329880 epoch=15 metrics={'time_sample_batch': 0.00039508371797202933, 'time_algorithm_update': 0.009781392932848828, 'loss': 0.014838246528901422, 'time_step': 0.010849759031443564, 'init_value': -1.1998211145401, 'ave_value': -1.199792116111952, 'soft_opc': nan} step=329880\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:15.21 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_329880.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "979de4fe119d4ae0b9acf49fed77285b",
       "version_major": 2,
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     },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:19.53 [info     ] FQE_20220408101531: epoch=16 step=351872 epoch=16 metrics={'time_sample_batch': 0.00036232762659363593, 'time_algorithm_update': 0.009918521679111203, 'loss': 0.014406733138312314, 'time_step': 0.010970824683696931, 'init_value': -1.1943985223770142, 'ave_value': -1.1944454668964195, 'soft_opc': nan} step=351872\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:19.53 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_351872.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8da3fe69b764cc9ba270876da559693",
       "version_major": 2,
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     },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:24.26 [info     ] FQE_20220408101531: epoch=17 step=373864 epoch=17 metrics={'time_sample_batch': 0.0003744374409117409, 'time_algorithm_update': 0.00995922580811274, 'loss': 0.01428214350057523, 'time_step': 0.010988600592736375, 'init_value': -1.2051113843917847, 'ave_value': -1.205107509673067, 'soft_opc': nan} step=373864\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:24.26 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_373864.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "65edd8cdc192430e92863b0715f4d0d3",
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     "text": [
      "2022-04-08 11:29.00 [info     ] FQE_20220408101531: epoch=18 step=395856 epoch=18 metrics={'time_sample_batch': 0.00038203330376487333, 'time_algorithm_update': 0.009992525821687873, 'loss': 0.01538582366787415, 'time_step': 0.011040078787076859, 'init_value': -1.2083408832550049, 'ave_value': -1.2083175075438217, 'soft_opc': nan} step=395856\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:29.00 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_395856.pt\n"
     ]
    },
    {
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      ]
     },
     "metadata": {},
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    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 11:33.36 [info     ] FQE_20220408101531: epoch=19 step=417848 epoch=19 metrics={'time_sample_batch': 0.0003776499231150472, 'time_algorithm_update': 0.010087388912171787, 'loss': 0.015587125753716942, 'time_step': 0.011116117564232924, 'init_value': -1.291703701019287, 'ave_value': -1.2917355034870188, 'soft_opc': nan} step=417848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:33.36 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_417848.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6fc3ad78438e433e9202e4b50f082055",
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:38.14 [info     ] FQE_20220408101531: epoch=20 step=439840 epoch=20 metrics={'time_sample_batch': 0.00038141430660177034, 'time_algorithm_update': 0.01013614619459053, 'loss': 0.01642687688007645, 'time_step': 0.01120726348444001, 'init_value': -1.3033894300460815, 'ave_value': -1.3033330699220398, 'soft_opc': nan} step=439840\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:38.14 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_439840.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52414c12fc0a40d8817df045806b534b",
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     },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:42.54 [info     ] FQE_20220408101531: epoch=21 step=461832 epoch=21 metrics={'time_sample_batch': 0.0003627845377174452, 'time_algorithm_update': 0.010275525205913306, 'loss': 0.015841113109846715, 'time_step': 0.011290682190415988, 'init_value': -1.169848084449768, 'ave_value': -1.1698074653602528, 'soft_opc': nan} step=461832\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:42.54 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_461832.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d22b6fd2a5a4e18acaf482c18efbbd4",
       "version_major": 2,
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     },
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:47.32 [info     ] FQE_20220408101531: epoch=22 step=483824 epoch=22 metrics={'time_sample_batch': 0.0003991935547155309, 'time_algorithm_update': 0.010175608903462342, 'loss': 0.014962510315741714, 'time_step': 0.011254640785986052, 'init_value': -1.232766032218933, 'ave_value': -1.2326975976731844, 'soft_opc': nan} step=483824\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:47.32 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_483824.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "910e9947bb4f4b90abe67186a8d37cdb",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:52.11 [info     ] FQE_20220408101531: epoch=23 step=505816 epoch=23 metrics={'time_sample_batch': 0.0003740067762129955, 'time_algorithm_update': 0.01019599491702552, 'loss': 0.015272819416004188, 'time_step': 0.011222728472182688, 'init_value': -1.2490142583847046, 'ave_value': -1.2489793407124297, 'soft_opc': nan} step=505816\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:52.11 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_505816.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f703f5af838455b98e7492ed49d501a",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:56.52 [info     ] FQE_20220408101531: epoch=24 step=527808 epoch=24 metrics={'time_sample_batch': 0.00037722665208085924, 'time_algorithm_update': 0.01034557153719822, 'loss': 0.015330276761558743, 'time_step': 0.011372585498709903, 'init_value': -1.2209209203720093, 'ave_value': -1.2208193116043171, 'soft_opc': nan} step=527808\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 11:56.52 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_527808.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fb090813a1604b50a36e5383fa0216fd",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:01.35 [info     ] FQE_20220408101531: epoch=25 step=549800 epoch=25 metrics={'time_sample_batch': 0.00036412851512974156, 'time_algorithm_update': 0.010422165457141664, 'loss': 0.01598359552031045, 'time_step': 0.011432539120115943, 'init_value': -1.275980830192566, 'ave_value': -1.2758916281015589, 'soft_opc': nan} step=549800\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:01.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_549800.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "400629743818433fa01f22388f4f3fdd",
       "version_major": 2,
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     },
     "metadata": {},
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     "text": [
      "2022-04-08 12:06.21 [info     ] FQE_20220408101531: epoch=26 step=571792 epoch=26 metrics={'time_sample_batch': 0.0004066187778617218, 'time_algorithm_update': 0.010525358031124754, 'loss': 0.01530209168822247, 'time_step': 0.011579620833828822, 'init_value': -1.1911277770996094, 'ave_value': -1.1910750424994203, 'soft_opc': nan} step=571792\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:06.21 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_571792.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0566cd6482c3434aaee18a4b6424dff0",
       "version_major": 2,
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      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:11.04 [info     ] FQE_20220408101531: epoch=27 step=593784 epoch=27 metrics={'time_sample_batch': 0.00036039408574151143, 'time_algorithm_update': 0.010449083122872318, 'loss': 0.015500339997666921, 'time_step': 0.011442454566344993, 'init_value': -1.1870168447494507, 'ave_value': -1.186933588020294, 'soft_opc': nan} step=593784\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:11.04 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_593784.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ff5193d0f054d829958a765c87f9797",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:15.45 [info     ] FQE_20220408101531: epoch=28 step=615776 epoch=28 metrics={'time_sample_batch': 0.00035879855027585604, 'time_algorithm_update': 0.010388076576418164, 'loss': 0.01600205843245359, 'time_step': 0.011380769569859065, 'init_value': -1.2194031476974487, 'ave_value': -1.2193073361700875, 'soft_opc': nan} step=615776\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:15.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_615776.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6db04484cc984bf4946ea96d00ad15f1",
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:20.27 [info     ] FQE_20220408101531: epoch=29 step=637768 epoch=29 metrics={'time_sample_batch': 0.0003691882751516187, 'time_algorithm_update': 0.010393462644225252, 'loss': 0.014929111543583956, 'time_step': 0.011391606427140998, 'init_value': -1.2406582832336426, 'ave_value': -1.240550970992641, 'soft_opc': nan} step=637768\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:20.27 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_637768.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52a501ec7e8e4985b8b31b46a85ca349",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:25.11 [info     ] FQE_20220408101531: epoch=30 step=659760 epoch=30 metrics={'time_sample_batch': 0.0003781930755901094, 'time_algorithm_update': 0.010459870577031979, 'loss': 0.01659551338828585, 'time_step': 0.011503374958177097, 'init_value': -1.3277769088745117, 'ave_value': -1.327732560858772, 'soft_opc': nan} step=659760\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:25.11 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_659760.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d20ebd8f1838466cadc08908ab5bc19b",
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:29.58 [info     ] FQE_20220408101531: epoch=31 step=681752 epoch=31 metrics={'time_sample_batch': 0.00040786431762859055, 'time_algorithm_update': 0.010588652277131565, 'loss': 0.015615969580082796, 'time_step': 0.011671441030224786, 'init_value': -1.1669950485229492, 'ave_value': -1.1669357978392219, 'soft_opc': nan} step=681752\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:29.58 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_681752.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01174247ced144b389e1a4a71af8a34b",
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     "text": [
      "2022-04-08 12:34.45 [info     ] FQE_20220408101531: epoch=32 step=703744 epoch=32 metrics={'time_sample_batch': 0.000382251275934363, 'time_algorithm_update': 0.010628813893285913, 'loss': 0.015787606790232157, 'time_step': 0.01164645871581837, 'init_value': -1.286812663078308, 'ave_value': -1.286785836159481, 'soft_opc': nan} step=703744\n"
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     "text": [
      "2022-04-08 12:34.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_703744.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "172f4bbc7b3b4107b5846cb6e71d408a",
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     "text": [
      "2022-04-08 12:39.33 [info     ] FQE_20220408101531: epoch=33 step=725736 epoch=33 metrics={'time_sample_batch': 0.0003887681732961766, 'time_algorithm_update': 0.010661597033334498, 'loss': 0.01625863429416741, 'time_step': 0.011710565321746415, 'init_value': -1.2790303230285645, 'ave_value': -1.278940926040821, 'soft_opc': nan} step=725736\n"
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:39.33 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_725736.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4201d9714af41459dac1abd357484f1",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:44.21 [info     ] FQE_20220408101531: epoch=34 step=747728 epoch=34 metrics={'time_sample_batch': 0.0003883243257585722, 'time_algorithm_update': 0.010679927824973713, 'loss': 0.01630106943089238, 'time_step': 0.011726495893533206, 'init_value': -1.2428741455078125, 'ave_value': -1.2428395448060978, 'soft_opc': nan} step=747728\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:44.21 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_747728.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b8edb5f9b88e41eaafd57ec6685e4513",
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     "text": [
      "2022-04-08 12:49.11 [info     ] FQE_20220408101531: epoch=35 step=769720 epoch=35 metrics={'time_sample_batch': 0.0003916657851279194, 'time_algorithm_update': 0.010682084812776268, 'loss': 0.01639387184197003, 'time_step': 0.011736918261112755, 'init_value': -1.2327381372451782, 'ave_value': -1.2326644746887354, 'soft_opc': nan} step=769720\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:49.11 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_769720.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "01247c24f7174920a637428b848a6cbc",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:53.58 [info     ] FQE_20220408101531: epoch=36 step=791712 epoch=36 metrics={'time_sample_batch': 0.00037975765957143706, 'time_algorithm_update': 0.010579572065651048, 'loss': 0.01577876313677914, 'time_step': 0.01162693157071155, 'init_value': -1.2238786220550537, 'ave_value': -1.2237758103046827, 'soft_opc': nan} step=791712\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:53.58 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_791712.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e819b9fc3fdf4353b059ace70ea12e30",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:58.45 [info     ] FQE_20220408101531: epoch=37 step=813704 epoch=37 metrics={'time_sample_batch': 0.0003939866260772794, 'time_algorithm_update': 0.010539354617572256, 'loss': 0.015422168284340064, 'time_step': 0.011622724662307567, 'init_value': -1.2989387512207031, 'ave_value': -1.2988755545998099, 'soft_opc': nan} step=813704\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 12:58.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_813704.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36f43fbaceb485cb0c3605b8491fe73",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:03.32 [info     ] FQE_20220408101531: epoch=38 step=835696 epoch=38 metrics={'time_sample_batch': 0.00039032606828772143, 'time_algorithm_update': 0.010564566298197207, 'loss': 0.016692986195376718, 'time_step': 0.011617210159391956, 'init_value': -1.2697631120681763, 'ave_value': -1.2696380816535526, 'soft_opc': nan} step=835696\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:03.32 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_835696.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6428331d84a471eb9035054936fee37",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:08.22 [info     ] FQE_20220408101531: epoch=39 step=857688 epoch=39 metrics={'time_sample_batch': 0.0003771254174186343, 'time_algorithm_update': 0.010699721217892654, 'loss': 0.016985949493373946, 'time_step': 0.011746668867650314, 'init_value': -1.3316798210144043, 'ave_value': -1.331606647509581, 'soft_opc': nan} step=857688\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:08.22 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_857688.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "463f74e518e2467aa8a6d0472e48e233",
       "version_major": 2,
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:13.11 [info     ] FQE_20220408101531: epoch=40 step=879680 epoch=40 metrics={'time_sample_batch': 0.0004118317558618839, 'time_algorithm_update': 0.010665733322484921, 'loss': 0.01704987189422773, 'time_step': 0.011746473293297505, 'init_value': -1.3140630722045898, 'ave_value': -1.3139170348126743, 'soft_opc': nan} step=879680\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:13.11 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_879680.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d1f5e920059e4c1f8fb6a9d5d573529d",
       "version_major": 2,
       "version_minor": 0
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:18.00 [info     ] FQE_20220408101531: epoch=41 step=901672 epoch=41 metrics={'time_sample_batch': 0.0003864474515994708, 'time_algorithm_update': 0.01070834421469715, 'loss': 0.017213841057155947, 'time_step': 0.011731143646476138, 'init_value': -1.257326364517212, 'ave_value': -1.2572564576441834, 'soft_opc': nan} step=901672\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:18.00 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_901672.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a2a840d4e7574f2ab96cdbe08a63a153",
       "version_major": 2,
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:22.49 [info     ] FQE_20220408101531: epoch=42 step=923664 epoch=42 metrics={'time_sample_batch': 0.00037120517360899566, 'time_algorithm_update': 0.01070023801553115, 'loss': 0.01664899390805755, 'time_step': 0.011763267828881329, 'init_value': -1.2693506479263306, 'ave_value': -1.269284359583138, 'soft_opc': nan} step=923664\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:22.49 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_923664.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "00dfd41a26eb4d8a8c073240017ed3f4",
       "version_major": 2,
       "version_minor": 0
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:27.39 [info     ] FQE_20220408101531: epoch=43 step=945656 epoch=43 metrics={'time_sample_batch': 0.00038007386340449707, 'time_algorithm_update': 0.010724077022218757, 'loss': 0.016771692367774274, 'time_step': 0.011780811943284552, 'init_value': -1.2042527198791504, 'ave_value': -1.204107349382153, 'soft_opc': nan} step=945656\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:27.39 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_945656.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ed41eb3e2224d438fe49a46354ff139",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:32.28 [info     ] FQE_20220408101531: epoch=44 step=967648 epoch=44 metrics={'time_sample_batch': 0.0003633341081535309, 'time_algorithm_update': 0.01071794575541702, 'loss': 0.016763283371185215, 'time_step': 0.011752058036026412, 'init_value': -1.244667887687683, 'ave_value': -1.2445336135525151, 'soft_opc': nan} step=967648\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:32.28 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_967648.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e21fc120202145e88fceeceb1f130975",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:37.18 [info     ] FQE_20220408101531: epoch=45 step=989640 epoch=45 metrics={'time_sample_batch': 0.00037082945186036335, 'time_algorithm_update': 0.010701758476030441, 'loss': 0.01630205701111403, 'time_step': 0.011761532952109872, 'init_value': -1.2385843992233276, 'ave_value': -1.2384167397445625, 'soft_opc': nan} step=989640\n"
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     "text": [
      "2022-04-08 13:37.18 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_989640.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "46f2e2a807034335b2109546441b7d4c",
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     "text": [
      "2022-04-08 13:42.11 [info     ] FQE_20220408101531: epoch=46 step=1011632 epoch=46 metrics={'time_sample_batch': 0.0003587391190894867, 'time_algorithm_update': 0.010902628318835884, 'loss': 0.017455006916773742, 'time_step': 0.011920587578093889, 'init_value': -1.3894442319869995, 'ave_value': -1.389243624310786, 'soft_opc': nan} step=1011632\n"
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     "text": [
      "2022-04-08 13:42.11 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1011632.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "123046e1185745f9be7c8902b82e94fd",
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     "text": [
      "2022-04-08 13:47.02 [info     ] FQE_20220408101531: epoch=47 step=1033624 epoch=47 metrics={'time_sample_batch': 0.0003875097108780579, 'time_algorithm_update': 0.010765515378970759, 'loss': 0.01792004111571055, 'time_step': 0.011833720399753186, 'init_value': -1.3034937381744385, 'ave_value': -1.3033472578706653, 'soft_opc': nan} step=1033624\n"
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     "text": [
      "2022-04-08 13:47.02 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1033624.pt\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:51.55 [info     ] FQE_20220408101531: epoch=48 step=1055616 epoch=48 metrics={'time_sample_batch': 0.00035919793825576243, 'time_algorithm_update': 0.010883191784063917, 'loss': 0.018132026734343214, 'time_step': 0.011902585262469961, 'init_value': -1.3212393522262573, 'ave_value': -1.3210465416814068, 'soft_opc': nan} step=1055616\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:51.55 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1055616.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eac9ea9317674b91a9c7a6ea190a5329",
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:56.46 [info     ] FQE_20220408101531: epoch=49 step=1077608 epoch=49 metrics={'time_sample_batch': 0.00037757137898056237, 'time_algorithm_update': 0.010833779837097329, 'loss': 0.018059169751885557, 'time_step': 0.011889417796362179, 'init_value': -1.3076229095458984, 'ave_value': -1.3074006540937348, 'soft_opc': nan} step=1077608\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 13:56.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1077608.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "88aac9e2f0ba418b9915e320e57fa54a",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:01.39 [info     ] FQE_20220408101531: epoch=50 step=1099600 epoch=50 metrics={'time_sample_batch': 0.00037121656765804243, 'time_algorithm_update': 0.010823299707868569, 'loss': 0.017747067851574597, 'time_step': 0.011851452131104842, 'init_value': -1.3062236309051514, 'ave_value': -1.3060754774612993, 'soft_opc': nan} step=1099600\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:01.39 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1099600.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bf78d6b5c9e6427eb6b68bb42ab16d67",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:06.31 [info     ] FQE_20220408101531: epoch=51 step=1121592 epoch=51 metrics={'time_sample_batch': 0.0003807711553556263, 'time_algorithm_update': 0.010856462288180366, 'loss': 0.016974813595250555, 'time_step': 0.011900548329566687, 'init_value': -1.3001558780670166, 'ave_value': -1.2999145561840055, 'soft_opc': nan} step=1121592\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:06.31 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1121592.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "73049115da494b90bce7d45b40ff643d",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:11.24 [info     ] FQE_20220408101531: epoch=52 step=1143584 epoch=52 metrics={'time_sample_batch': 0.0003656212114429162, 'time_algorithm_update': 0.010876524454899466, 'loss': 0.017726033157240712, 'time_step': 0.01192723538234651, 'init_value': -1.3225984573364258, 'ave_value': -1.3223839074138337, 'soft_opc': nan} step=1143584\n"
     ]
    },
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:11.24 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1143584.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14d467f304ac4357a6d3318ee4e2564a",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:16.18 [info     ] FQE_20220408101531: epoch=53 step=1165576 epoch=53 metrics={'time_sample_batch': 0.0003766310392791204, 'time_algorithm_update': 0.010905233154560531, 'loss': 0.018000469613303764, 'time_step': 0.011932454182044425, 'init_value': -1.2647055387496948, 'ave_value': -1.2644646277233997, 'soft_opc': nan} step=1165576\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:16.18 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1165576.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec8b26886596455db164a5a2b8d71a21",
       "version_major": 2,
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     "text": [
      "2022-04-08 14:21.12 [info     ] FQE_20220408101531: epoch=54 step=1187568 epoch=54 metrics={'time_sample_batch': 0.00037430367195723317, 'time_algorithm_update': 0.010879456660842063, 'loss': 0.016986674874604722, 'time_step': 0.011914880156907311, 'init_value': -1.3543647527694702, 'ave_value': -1.3542024727526079, 'soft_opc': nan} step=1187568\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:21.12 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1187568.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6d56bf91b9b46d6a4fe8783a0f23043",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:26.05 [info     ] FQE_20220408101531: epoch=55 step=1209560 epoch=55 metrics={'time_sample_batch': 0.0003656069661713202, 'time_algorithm_update': 0.010881762530162752, 'loss': 0.017530619784846106, 'time_step': 0.011925981332276596, 'init_value': -1.2456320524215698, 'ave_value': -1.2453618729389397, 'soft_opc': nan} step=1209560\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:26.05 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1209560.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5c27cd02efca429fa192e6a7cd7cb757",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:31.00 [info     ] FQE_20220408101531: epoch=56 step=1231552 epoch=56 metrics={'time_sample_batch': 0.00036628473321061953, 'time_algorithm_update': 0.010945900347762733, 'loss': 0.018722360984760114, 'time_step': 0.011974704042266785, 'init_value': -1.3238378763198853, 'ave_value': -1.3234220352782047, 'soft_opc': nan} step=1231552\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:31.00 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1231552.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac19d8c4573844dc98d5bc924c3c7511",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:35.55 [info     ] FQE_20220408101531: epoch=57 step=1253544 epoch=57 metrics={'time_sample_batch': 0.0003790240931311448, 'time_algorithm_update': 0.010916038951946632, 'loss': 0.018058412619071398, 'time_step': 0.011954842566923472, 'init_value': -1.3310996294021606, 'ave_value': -1.3307951850279602, 'soft_opc': nan} step=1253544\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:35.55 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1253544.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "baf6cae24a434534bdaa42dff12bff90",
       "version_major": 2,
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:40.51 [info     ] FQE_20220408101531: epoch=58 step=1275536 epoch=58 metrics={'time_sample_batch': 0.00035227125625083037, 'time_algorithm_update': 0.011017043391502133, 'loss': 0.018746885805488826, 'time_step': 0.012030413537599599, 'init_value': -1.3532549142837524, 'ave_value': -1.3529047156405258, 'soft_opc': nan} step=1275536\n"
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    },
    {
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     "text": [
      "2022-04-08 14:40.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1275536.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d0f511c17bf424dbbc688f78fdbd147",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:45.48 [info     ] FQE_20220408101531: epoch=59 step=1297528 epoch=59 metrics={'time_sample_batch': 0.0003581941886657626, 'time_algorithm_update': 0.011030638042299389, 'loss': 0.01917164723850927, 'time_step': 0.012039386421691379, 'init_value': -1.358017086982727, 'ave_value': -1.3576172695947026, 'soft_opc': nan} step=1297528\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:45.48 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1297528.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d426341a5eb42f2aa68999eada934b7",
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     "text": [
      "2022-04-08 14:50.45 [info     ] FQE_20220408101531: epoch=60 step=1319520 epoch=60 metrics={'time_sample_batch': 0.00035650866081082377, 'time_algorithm_update': 0.011006095726822193, 'loss': 0.018870322118286386, 'time_step': 0.01202577186654203, 'init_value': -1.3100930452346802, 'ave_value': -1.3096999373682672, 'soft_opc': nan} step=1319520\n"
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     "text": [
      "2022-04-08 14:50.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1319520.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f951a93877ef46e0bd358d8fe7adc75b",
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     "output_type": "stream",
     "text": [
      "2022-04-08 14:55.41 [info     ] FQE_20220408101531: epoch=61 step=1341512 epoch=61 metrics={'time_sample_batch': 0.0003762123432103917, 'time_algorithm_update': 0.011015882141679449, 'loss': 0.018982398267002248, 'time_step': 0.012065792666994125, 'init_value': -1.386768102645874, 'ave_value': -1.386364795019801, 'soft_opc': nan} step=1341512\n"
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     "text": [
      "2022-04-08 14:55.41 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1341512.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "96cbd4a3dba1493c82588d7eea5d3681",
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     "text": [
      "2022-04-08 15:00.38 [info     ] FQE_20220408101531: epoch=62 step=1363504 epoch=62 metrics={'time_sample_batch': 0.0003629084954308614, 'time_algorithm_update': 0.011127770651726777, 'loss': 0.018887747919567842, 'time_step': 0.012140472169874711, 'init_value': -1.2839354276657104, 'ave_value': -1.2836335102276968, 'soft_opc': nan} step=1363504\n"
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     "text": [
      "2022-04-08 15:00.38 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1363504.pt\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 15:05.38 [info     ] FQE_20220408101531: epoch=63 step=1385496 epoch=63 metrics={'time_sample_batch': 0.000375477844431167, 'time_algorithm_update': 0.011139656292737376, 'loss': 0.019408594092975285, 'time_step': 0.012201447472133477, 'init_value': -1.3572402000427246, 'ave_value': -1.356995996464854, 'soft_opc': nan} step=1385496\n"
     ]
    },
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     "text": [
      "2022-04-08 15:05.38 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1385496.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d80b1924986241308df7e3cc4064f05a",
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     "text": [
      "2022-04-08 15:10.38 [info     ] FQE_20220408101531: epoch=64 step=1407488 epoch=64 metrics={'time_sample_batch': 0.00036291823078390034, 'time_algorithm_update': 0.011162676185467062, 'loss': 0.01870864128581488, 'time_step': 0.012195990112921506, 'init_value': -1.2768232822418213, 'ave_value': -1.2764027142407819, 'soft_opc': nan} step=1407488\n"
     ]
    },
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      "2022-04-08 15:10.38 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1407488.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72aa8b1e8772480fa53494012db4476d",
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     "text": [
      "2022-04-08 15:15.37 [info     ] FQE_20220408101531: epoch=65 step=1429480 epoch=65 metrics={'time_sample_batch': 0.0003570453844837121, 'time_algorithm_update': 0.011156890133416796, 'loss': 0.018460958344318885, 'time_step': 0.012194641072691983, 'init_value': -1.308440923690796, 'ave_value': -1.3081041556725084, 'soft_opc': nan} step=1429480\n"
     ]
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      "2022-04-08 15:15.37 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1429480.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca144752bf3441e8a02d1bd1ac410713",
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     "text": [
      "2022-04-08 15:20.40 [info     ] FQE_20220408101531: epoch=66 step=1451472 epoch=66 metrics={'time_sample_batch': 0.00036120575182912307, 'time_algorithm_update': 0.01133137521807954, 'loss': 0.01814043719531159, 'time_step': 0.0123551675908216, 'init_value': -1.3011995553970337, 'ave_value': -1.3007865318194942, 'soft_opc': nan} step=1451472\n"
     ]
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     "text": [
      "2022-04-08 15:20.40 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1451472.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1b86dbab19f4cd28bf10a5cfa9380a1",
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     "text": [
      "2022-04-08 15:25.42 [info     ] FQE_20220408101531: epoch=67 step=1473464 epoch=67 metrics={'time_sample_batch': 0.0003899059520282933, 'time_algorithm_update': 0.011268395723191639, 'loss': 0.018965440216756953, 'time_step': 0.012334500910716301, 'init_value': -1.302665114402771, 'ave_value': -1.302239928988886, 'soft_opc': nan} step=1473464\n"
     ]
    },
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     "text": [
      "2022-04-08 15:25.42 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1473464.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6b2b927349594d0fa0f9cbcf47d45c94",
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     "text": [
      "2022-04-08 15:30.44 [info     ] FQE_20220408101531: epoch=68 step=1495456 epoch=68 metrics={'time_sample_batch': 0.0003832610532039727, 'time_algorithm_update': 0.01122981881219372, 'loss': 0.019655468817794595, 'time_step': 0.012331580261440007, 'init_value': -1.3877503871917725, 'ave_value': -1.3874085512455434, 'soft_opc': nan} step=1495456\n"
     ]
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      "2022-04-08 15:30.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1495456.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0df181d7118945b6ba08d542601891f8",
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     "text": [
      "2022-04-08 15:35.46 [info     ] FQE_20220408101531: epoch=69 step=1517448 epoch=69 metrics={'time_sample_batch': 0.00036377742447476683, 'time_algorithm_update': 0.011287381417884008, 'loss': 0.02064155583733833, 'time_step': 0.012294097255038626, 'init_value': -1.3518794775009155, 'ave_value': -1.351594885356834, 'soft_opc': nan} step=1517448\n"
     ]
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      "2022-04-08 15:35.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1517448.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6f723ce6cc524530ac3a1f3e7986f133",
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     "text": [
      "2022-04-08 15:40.47 [info     ] FQE_20220408101531: epoch=70 step=1539440 epoch=70 metrics={'time_sample_batch': 0.0003827440712659218, 'time_algorithm_update': 0.011210349092660448, 'loss': 0.019446036291277006, 'time_step': 0.012260285148884262, 'init_value': -1.3792451620101929, 'ave_value': -1.3787914291561072, 'soft_opc': nan} step=1539440\n"
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      "2022-04-08 15:40.47 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1539440.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ed4fb2604a74c59971367793cd91414",
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     "text": [
      "2022-04-08 15:45.47 [info     ] FQE_20220408101531: epoch=71 step=1561432 epoch=71 metrics={'time_sample_batch': 0.00038511035831766415, 'time_algorithm_update': 0.011180940499172162, 'loss': 0.019780629041406924, 'time_step': 0.012236388868399211, 'init_value': -1.2886590957641602, 'ave_value': -1.2882596295270674, 'soft_opc': nan} step=1561432\n"
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     "text": [
      "2022-04-08 15:45.47 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1561432.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d989cea3f024ccfacce519d84bf9539",
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     "output_type": "stream",
     "text": [
      "2022-04-08 15:50.46 [info     ] FQE_20220408101531: epoch=72 step=1583424 epoch=72 metrics={'time_sample_batch': 0.00037542938448897965, 'time_algorithm_update': 0.011158711923690829, 'loss': 0.0194564720295131, 'time_step': 0.012184841865277282, 'init_value': -1.3384792804718018, 'ave_value': -1.338080767710649, 'soft_opc': nan} step=1583424\n"
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      "2022-04-08 15:50.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1583424.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1a9823af45d245c9aef1886d989823f9",
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     "text": [
      "2022-04-08 15:55.44 [info     ] FQE_20220408101531: epoch=73 step=1605416 epoch=73 metrics={'time_sample_batch': 0.00038150883059191157, 'time_algorithm_update': 0.01115417909778305, 'loss': 0.019259221746088767, 'time_step': 0.012176845888087167, 'init_value': -1.2978966236114502, 'ave_value': -1.2974379195262369, 'soft_opc': nan} step=1605416\n"
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      "2022-04-08 15:55.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1605416.pt\n"
     ]
    },
    {
     "data": {
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     "text": [
      "2022-04-08 16:00.44 [info     ] FQE_20220408101531: epoch=74 step=1627408 epoch=74 metrics={'time_sample_batch': 0.00035284330039164855, 'time_algorithm_update': 0.011183387466065014, 'loss': 0.02011337156029361, 'time_step': 0.012209851672841054, 'init_value': -1.4251493215560913, 'ave_value': -1.4246760001512682, 'soft_opc': nan} step=1627408\n"
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      "2022-04-08 16:00.44 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1627408.pt\n"
     ]
    },
    {
     "data": {
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     "text": [
      "2022-04-08 16:05.45 [info     ] FQE_20220408101531: epoch=75 step=1649400 epoch=75 metrics={'time_sample_batch': 0.00041831658510088095, 'time_algorithm_update': 0.011136052499211194, 'loss': 0.020532397567049915, 'time_step': 0.012262756302383606, 'init_value': -1.4017254114151, 'ave_value': -1.4013069942657803, 'soft_opc': nan} step=1649400\n"
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      "2022-04-08 16:05.45 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1649400.pt\n"
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    },
    {
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     "text": [
      "2022-04-08 16:10.46 [info     ] FQE_20220408101531: epoch=76 step=1671392 epoch=76 metrics={'time_sample_batch': 0.0003996114376979813, 'time_algorithm_update': 0.011243362964912518, 'loss': 0.02115813148439453, 'time_step': 0.012300688219139818, 'init_value': -1.4321569204330444, 'ave_value': -1.4315830565428604, 'soft_opc': nan} step=1671392\n"
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      "2022-04-08 16:10.46 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1671392.pt\n"
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    {
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       "model_id": "d7d70e1658da451bb785dabca39fc009",
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      "2022-04-08 16:15.48 [info     ] FQE_20220408101531: epoch=77 step=1693384 epoch=77 metrics={'time_sample_batch': 0.0003675849232165412, 'time_algorithm_update': 0.011259681476424416, 'loss': 0.01936783557475496, 'time_step': 0.012289769554537137, 'init_value': -1.3395956754684448, 'ave_value': -1.3390961705998545, 'soft_opc': nan} step=1693384\n"
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      "2022-04-08 16:15.48 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1693384.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca42901378e44344a89f5b57ecd23bb6",
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     "text": [
      "2022-04-08 16:20.49 [info     ] FQE_20220408101531: epoch=78 step=1715376 epoch=78 metrics={'time_sample_batch': 0.0003998155765595892, 'time_algorithm_update': 0.01121123861989007, 'loss': 0.01813864467560893, 'time_step': 0.012280864091559556, 'init_value': -1.3258864879608154, 'ave_value': -1.3253045807336006, 'soft_opc': nan} step=1715376\n"
     ]
    },
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     "text": [
      "2022-04-08 16:20.49 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1715376.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c63a6860fbc64ea899b10729594448a9",
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     "text": [
      "2022-04-08 16:25.51 [info     ] FQE_20220408101531: epoch=79 step=1737368 epoch=79 metrics={'time_sample_batch': 0.00039475741018681754, 'time_algorithm_update': 0.011255247380873124, 'loss': 0.02025263439269478, 'time_step': 0.012323700859139902, 'init_value': -1.3790216445922852, 'ave_value': -1.378395254180384, 'soft_opc': nan} step=1737368\n"
     ]
    },
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     "text": [
      "2022-04-08 16:25.51 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1737368.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d0fb08e5b624e6986cb43299ebbd5ae",
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     "text": [
      "2022-04-08 16:30.52 [info     ] FQE_20220408101531: epoch=80 step=1759360 epoch=80 metrics={'time_sample_batch': 0.00039207000826089414, 'time_algorithm_update': 0.011223420690475442, 'loss': 0.020354956652457904, 'time_step': 0.01229720456724212, 'init_value': -1.4156057834625244, 'ave_value': -1.4151293058240293, 'soft_opc': nan} step=1759360\n"
     ]
    },
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     "text": [
      "2022-04-08 16:30.52 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1759360.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "938ecbe0c75d4e71842840102e8c4f3d",
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     "text": [
      "2022-04-08 16:35.55 [info     ] FQE_20220408101531: epoch=81 step=1781352 epoch=81 metrics={'time_sample_batch': 0.0003486506589415638, 'time_algorithm_update': 0.01132460362957191, 'loss': 0.02078803270970652, 'time_step': 0.012353794006227624, 'init_value': -1.3773541450500488, 'ave_value': -1.3766137801991596, 'soft_opc': nan} step=1781352\n"
     ]
    },
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      "2022-04-08 16:35.55 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1781352.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e9c3abc774a4949b853f16e99977705",
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     "text": [
      "2022-04-08 16:40.57 [info     ] FQE_20220408101531: epoch=82 step=1803344 epoch=82 metrics={'time_sample_batch': 0.0003957499500273271, 'time_algorithm_update': 0.011288333184157947, 'loss': 0.020880130492619872, 'time_step': 0.012333097426229582, 'init_value': -1.3595433235168457, 'ave_value': -1.3590943936268596, 'soft_opc': nan} step=1803344\n"
     ]
    },
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     "text": [
      "2022-04-08 16:40.57 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1803344.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1531125381714b96b82243e05707c383",
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     },
     "metadata": {},
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     "text": [
      "2022-04-08 16:45.59 [info     ] FQE_20220408101531: epoch=83 step=1825336 epoch=83 metrics={'time_sample_batch': 0.00038460442351184786, 'time_algorithm_update': 0.011244730207433523, 'loss': 0.020503146041550048, 'time_step': 0.012322391909484474, 'init_value': -1.4593223333358765, 'ave_value': -1.4588454467206888, 'soft_opc': nan} step=1825336\n"
     ]
    },
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     "text": [
      "2022-04-08 16:45.59 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1825336.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8fb4fae37e44d649fcf2bebd4509700",
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     "text": [
      "2022-04-08 16:51.01 [info     ] FQE_20220408101531: epoch=84 step=1847328 epoch=84 metrics={'time_sample_batch': 0.0003988729818988688, 'time_algorithm_update': 0.011244838131085531, 'loss': 0.02068963114136529, 'time_step': 0.012328664956850848, 'init_value': -1.3934898376464844, 'ave_value': -1.39298659181488, 'soft_opc': nan} step=1847328\n"
     ]
    },
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     "text": [
      "2022-04-08 16:51.01 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1847328.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e693712dd63471f9c9a70496ccdb968",
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     "output_type": "stream",
     "text": [
      "2022-04-08 16:56.04 [info     ] FQE_20220408101531: epoch=85 step=1869320 epoch=85 metrics={'time_sample_batch': 0.0004069148930431323, 'time_algorithm_update': 0.011272573815817918, 'loss': 0.020479335603524208, 'time_step': 0.01233550435675409, 'init_value': -1.4001010656356812, 'ave_value': -1.3994674717701636, 'soft_opc': nan} step=1869320\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-08 16:56.04 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1869320.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1bd2f7d59fd14680863e238292f8aa48",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-08 17:01.06 [info     ] FQE_20220408101531: epoch=86 step=1891312 epoch=86 metrics={'time_sample_batch': 0.00040059275694785007, 'time_algorithm_update': 0.011213017338273654, 'loss': 0.0203339119789477, 'time_step': 0.012326653749997418, 'init_value': -1.358876347541809, 'ave_value': -1.3583477062245983, 'soft_opc': nan} step=1891312\n"
     ]
    },
    {
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     "text": [
      "2022-04-08 17:01.06 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1891312.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4fb08189ab7b4a0d8d3b4cbc8b58cfbf",
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       "version_minor": 0
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     "output_type": "stream",
     "text": [
      "2022-04-08 17:06.08 [info     ] FQE_20220408101531: epoch=87 step=1913304 epoch=87 metrics={'time_sample_batch': 0.00035809604373139526, 'time_algorithm_update': 0.01130956085043068, 'loss': 0.020339359853559145, 'time_step': 0.01233559017530048, 'init_value': -1.402394413948059, 'ave_value': -1.4018925250330834, 'soft_opc': nan} step=1913304\n"
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     "text": [
      "2022-04-08 17:06.08 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1913304.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30fa441f798241e2b9a629acf7791e3b",
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     "text": [
      "2022-04-08 17:11.10 [info     ] FQE_20220408101531: epoch=88 step=1935296 epoch=88 metrics={'time_sample_batch': 0.00037772108442612497, 'time_algorithm_update': 0.01129963528940833, 'loss': 0.02098845026688921, 'time_step': 0.012347023317535472, 'init_value': -1.450186848640442, 'ave_value': -1.4495036299949293, 'soft_opc': nan} step=1935296\n"
     ]
    },
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     "output_type": "stream",
     "text": [
      "2022-04-08 17:11.10 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1935296.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "464371a772554da8a7411d11ff8922b9",
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     "text": [
      "2022-04-08 17:16.14 [info     ] FQE_20220408101531: epoch=89 step=1957288 epoch=89 metrics={'time_sample_batch': 0.0003948407461097692, 'time_algorithm_update': 0.011289617199167505, 'loss': 0.020281255311571045, 'time_step': 0.012390919460648405, 'init_value': -1.3632228374481201, 'ave_value': -1.3627095481679625, 'soft_opc': nan} step=1957288\n"
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     "text": [
      "2022-04-08 17:16.14 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1957288.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "278f69874c6a4a68b1fba9248f8cdfd3",
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     "text": [
      "2022-04-08 17:21.17 [info     ] FQE_20220408101531: epoch=90 step=1979280 epoch=90 metrics={'time_sample_batch': 0.00039197931119683613, 'time_algorithm_update': 0.011333477642372245, 'loss': 0.020753896746936805, 'time_step': 0.0123844071057286, 'init_value': -1.3583476543426514, 'ave_value': -1.3578825416486258, 'soft_opc': nan} step=1979280\n"
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     "text": [
      "2022-04-08 17:21.17 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_1979280.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "132d1e4091b24b5dbb7e83a2c2122950",
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     "text": [
      "2022-04-08 17:26.21 [info     ] FQE_20220408101531: epoch=91 step=2001272 epoch=91 metrics={'time_sample_batch': 0.00040606384105619926, 'time_algorithm_update': 0.011322322900878945, 'loss': 0.020185506504070135, 'time_step': 0.012412018225643149, 'init_value': -1.44951331615448, 'ave_value': -1.4487905443475242, 'soft_opc': nan} step=2001272\n"
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      "2022-04-08 17:26.21 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2001272.pt\n"
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    {
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       "model_id": "b376de6509a34646beec4589cfdab994",
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     "output_type": "stream",
     "text": [
      "2022-04-08 17:31.23 [info     ] FQE_20220408101531: epoch=92 step=2023264 epoch=92 metrics={'time_sample_batch': 0.0004168455385650102, 'time_algorithm_update': 0.011268751757411186, 'loss': 0.0216806577676526, 'time_step': 0.012371730906350173, 'init_value': -1.4495348930358887, 'ave_value': -1.448752344858767, 'soft_opc': nan} step=2023264\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:31.23 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2023264.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "72189435cc8241e3aa1f3329937f2b03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 93/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:36.28 [info     ] FQE_20220408101531: epoch=93 step=2045256 epoch=93 metrics={'time_sample_batch': 0.000422375056473894, 'time_algorithm_update': 0.011320411822890402, 'loss': 0.02085398148734087, 'time_step': 0.012416729258380834, 'init_value': -1.3919988870620728, 'ave_value': -1.3914394819298492, 'soft_opc': nan} step=2045256\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:36.28 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2045256.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "044ea728639241f7a968e5991dacfddb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 94/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:41.31 [info     ] FQE_20220408101531: epoch=94 step=2067248 epoch=94 metrics={'time_sample_batch': 0.00039339951273743655, 'time_algorithm_update': 0.011332354650969243, 'loss': 0.021543099441270985, 'time_step': 0.01239406787165298, 'init_value': -1.4453223943710327, 'ave_value': -1.4446857915357911, 'soft_opc': nan} step=2067248\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:41.31 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2067248.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5f951dba3513462ea2c75bccb3a329dd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 95/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:46.35 [info     ] FQE_20220408101531: epoch=95 step=2089240 epoch=95 metrics={'time_sample_batch': 0.00037591663999269557, 'time_algorithm_update': 0.01139372487465396, 'loss': 0.020782538862985325, 'time_step': 0.012454724292314543, 'init_value': -1.3417508602142334, 'ave_value': -1.3410881339551053, 'soft_opc': nan} step=2089240\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:46.35 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2089240.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "54ef6ddd239b4f2a839951199638352c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 96/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:51.41 [info     ] FQE_20220408101531: epoch=96 step=2111232 epoch=96 metrics={'time_sample_batch': 0.00036690532402282804, 'time_algorithm_update': 0.011408734805109545, 'loss': 0.02048873100988236, 'time_step': 0.012461476323386893, 'init_value': -1.394972801208496, 'ave_value': -1.3943627591657317, 'soft_opc': nan} step=2111232\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:51.41 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2111232.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb5ba1407bcf4ba2b365e40c21b393b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 97/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:56.48 [info     ] FQE_20220408101531: epoch=97 step=2133224 epoch=97 metrics={'time_sample_batch': 0.00039684395219421944, 'time_algorithm_update': 0.01143764678718134, 'loss': 0.021215572621518908, 'time_step': 0.012489814125611245, 'init_value': -1.429599642753601, 'ave_value': -1.429008664413845, 'soft_opc': nan} step=2133224\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 17:56.48 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2133224.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "845b1c4c97574c2cb90d7c15f8c49939",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 98/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:01.53 [info     ] FQE_20220408101531: epoch=98 step=2155216 epoch=98 metrics={'time_sample_batch': 0.0003764577218080356, 'time_algorithm_update': 0.011412908257723462, 'loss': 0.021983982455997465, 'time_step': 0.012457663577528337, 'init_value': -1.4113314151763916, 'ave_value': -1.4106843986786632, 'soft_opc': nan} step=2155216\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:01.53 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2155216.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "746beb3ed345485d8a51a8509a98ba92",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 99/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:06.57 [info     ] FQE_20220408101531: epoch=99 step=2177208 epoch=99 metrics={'time_sample_batch': 0.00036088775920625077, 'time_algorithm_update': 0.011380922907090005, 'loss': 0.021368039876755666, 'time_step': 0.012450439197534038, 'init_value': -1.3978217840194702, 'ave_value': -1.3972072687280406, 'soft_opc': nan} step=2177208\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:06.57 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2177208.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "959b8862b60f4715a2af96029cbb95d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 100/100:   0%|          | 0/21992 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:12.02 [info     ] FQE_20220408101531: epoch=100 step=2199200 epoch=100 metrics={'time_sample_batch': 0.00040322245143031415, 'time_algorithm_update': 0.011376602665299978, 'loss': 0.021428368828787916, 'time_step': 0.012449288929796687, 'init_value': -1.385913610458374, 'ave_value': -1.3852465892698747, 'soft_opc': nan} step=2199200\n"
     ]
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-08 18:12.02 [info     ] Model parameters are saved to d3rlpy_logs\\FQE_20220408101531\\model_2199200.pt\n"
     ]
    },
    {
     "data": {
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       " (2,\n",
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       " (3,\n",
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       " (4,\n",
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       " (5,\n",
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       " (10,\n",
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       " (11,\n",
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       " (12,\n",
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       " (14,\n",
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       " (19,\n",
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       " (20,\n",
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       " (21,\n",
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       " (100,\n",
       "  {'time_sample_batch': 0.00040322245143031415,\n",
       "   'time_algorithm_update': 0.011376602665299978,\n",
       "   'loss': 0.021428368828787916,\n",
       "   'time_step': 0.012449288929796687,\n",
       "   'init_value': -1.385913610458374,\n",
       "   'ave_value': -1.3852465892698747,\n",
       "   'soft_opc': nan})]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from d3rlpy.ope import FQE\n",
    "# metrics to evaluate with\n",
    "from d3rlpy.metrics.scorer import soft_opc_scorer\n",
    "\n",
    "\n",
    "ope_dataset = get_dataset([i*2 for i in range(100)], path=\"collected_data/rl_stochastic.txt\") #change if you'd prefer different chunks\n",
    "ope_train_episodes, ope_test_episodes = train_test_split(ope_dataset, test_size=0.2)\n",
    "\n",
    "fqe = FQE(algo=model, action_scaler = action_scaler, use_gpu=False) #change this if you have one!\n",
    "fqe.fit(ope_train_episodes, eval_episodes=ope_test_episodes,\n",
    "        tensorboard_dir='runs',\n",
    "        n_epochs=100, n_steps_per_epoch=10000, #change if overfitting/underfitting\n",
    "        scorers={\n",
    "           'init_value': initial_state_value_estimation_scorer,\n",
    "            'ave_value': average_value_estimation_scorer,\n",
    "           'soft_opc': soft_opc_scorer(return_threshold=0)\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8af905f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model('cqlDet100model.pt')\n",
    "model.save_policy('cqlDet100.pt')"
   ]
  }
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