workflow_old_net_coarse_saved.ipynb

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  {
   "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=\"collected_data/rl_stochastic_coarse.txt\")\n",
    "    samples.setting(\"coarse\")\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[::1111] = 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"
   ]
  },
  {
   "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": 3,
   "id": "f543168b-2603-47ed-bff8-d0bafb40bb0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stdout",
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[ 0.00000000e+00  7.95731469e+08 -4.45389108e-01  1.16000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  3.60110892e-01 -2.97999953e-02\n",
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     ]
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  3.88610892e-01 -5.03999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  2.41610892e-01 -1.07999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  3.46310892e-01 -3.27999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[ 0.00000000e+00  7.95731469e+08 -3.20889108e-01 -5.65999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[ 0.00000000e+00  7.95731469e+08  3.89210892e-01 -3.77999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  2.34110892e-01 -3.35999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.47310892e-01  4.58000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  8.89108923e-02 -2.91999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  2.56910892e-01  9.00000469e-03\n",
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      "[ 0.00000000e+00  7.95731469e+08  4.84108923e-02  4.12000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.81989108e-01  1.36000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -1.02489108e-01 -3.83999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -6.16891077e-02 -1.33999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.11489108e-01  4.69199999e-09\n",
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      "start\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  3.10910892e-01  1.60000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -4.07889108e-01 -2.19999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  2.94710892e-01  5.28000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  8.71108923e-02  2.70000047e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  4.45910892e-01  3.76000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  9.37108923e-02  5.08000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  1.49210892e-01  2.00000469e-03\n",
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      "[ 0.00000000e+00  7.95731469e+08 -9.04891077e-02  2.80000469e-03\n",
      "  1.02999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
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      "[ 0.00000000e+00  7.95731469e+08 -2.07789108e-01 -4.75999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -4.42891077e-02  5.36000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.61010892e-01  2.76000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -2.07489108e-01  4.00000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -1.57891077e-02  3.40000469e-03\n",
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      "start\n",
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      "start\n",
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      "[ 0.00000000e+00  7.95731469e+08  8.32108923e-02  3.64000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  3.68810892e-01  4.36000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  4.03108923e-02 -7.99995308e-04\n",
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      "start\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  4.23110892e-01  3.76000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[ 0.00000000e+00  7.95731469e+08  1.12310892e-01 -4.91999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  1.21108923e-02 -5.39999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.71189108e-01 -1.65999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08  1.97210892e-01 -2.69999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  1.28210892e-01 -5.85999953e-02\n",
      " -2.96000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01 -2.64879576e-01]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  3.43610892e-01 -5.79999953e-02\n",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.15689108e-01  4.48000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -4.15089108e-01 -3.33999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -1.37589108e-01 -2.15999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -2.65689108e-01 -4.91999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -3.66789108e-01  1.44000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  3.01610892e-01 -4.25999953e-02\n",
      "  2.79998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.93110892e-01 -1.47999953e-02\n",
      "  1.04999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01  6.00000000e-01]\n",
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      "start\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08  5.51089229e-03  3.00000469e-03\n",
      "  7.09998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
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      "[ 0.00000000e+00  7.95731469e+08 -3.64989108e-01  2.28000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  3.70108923e-02  2.80000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -2.10189108e-01  5.62000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  1.27910892e-01 -4.63999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -3.16089108e-01 -3.11999953e-02\n",
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      "start\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -2.53089108e-01 -2.23999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08  1.11710892e-01  4.40000047e-02\n",
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      "  6.00000000e-01 -4.05363783e-01 -6.00000000e-01]\n",
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      "[ 0.00000000e+00  7.95731469e+08  2.96510892e-01  5.12000047e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read chunk # 1881 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.83210892e-01  1.08000047e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.14410892e-01 -3.43999953e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.36210892e-01 -2.21999953e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.82810892e-01 -2.45999953e-02\n",
      "  1.37999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
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      "Read chunk # 1885 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.71108923e-02  1.14000047e-02\n",
      " -1.65000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01 -2.53657725e-01]\n",
      "Read chunk # 1886 out of 4999\n",
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      "[ 0.00000000e+00  7.95731469e+08  7.69108923e-02 -1.67999953e-02\n",
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      "  2.72119403e-01 -1.35015674e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -2.69889108e-01  2.20000047e-02\n",
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      "  6.00000000e-01 -4.91012867e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.57910892e-01  5.48000047e-02\n",
      " -2.54000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.40838507e-01 -6.00000000e-01 -6.00000000e-01]\n",
      "Read chunk # 1889 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.02710892e-01 -3.29999953e-02\n",
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      "Read chunk # 1890 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -2.29689108e-01 -2.53999953e-02\n",
      " -2.20001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01  4.62950510e-01  6.00000000e-01]\n",
      "Read chunk # 1891 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.80589108e-01 -2.61999953e-02\n",
      " -2.18000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -5.58136803e-01 -6.00000000e-01 -6.00000000e-01]\n",
      "Read chunk # 1892 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.23610892e-01 -3.73999953e-02\n",
      "  2.31999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -1.34001678e-01  6.00000000e-01  2.10457163e-01]\n",
      "Read chunk # 1893 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.12891077e-02  3.80000469e-03\n",
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      " -6.00000000e-01 -5.66448160e-01 -5.70504183e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.38810892e-01  5.24000047e-02\n",
      "  2.23999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -2.64712632e-01 -6.00000000e-01  6.00000000e-01]\n",
      "Read chunk # 1895 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.06310892e-01 -2.67999953e-02\n",
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      " -6.00000000e-01  6.00000000e-01 -1.50841933e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.49010892e-01 -2.77999953e-02\n",
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      "  6.00000000e-01  3.90548874e-01  5.91093903e-02]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.41289108e-01  3.16000047e-02\n",
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      " -6.00000000e-01  2.10377453e-01 -3.43948428e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.69810892e-01 -2.71999953e-02\n",
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      "  6.00000000e-01 -1.39046876e-01 -1.29209941e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -5.08891077e-02  1.30000047e-02\n",
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      " -6.00000000e-01  5.08233452e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -2.42289108e-01  1.12000047e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.10510892e-01 -5.41999953e-02\n",
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      "  5.34468056e-02  6.00000000e-01 -4.97393212e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.42910892e-01  3.10000047e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  4.00108923e-02  1.06000047e-02\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.46589108e-01 -2.69999953e-02\n",
      "  3.89998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  3.62192103e-01  6.00000000e-01  1.42748122e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.48089108e-01  2.60000047e-02\n",
      "  4.99986580e-04  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -5.68199209e-01  3.45716051e-01]\n",
      "Read chunk # 1906 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.02489108e-01 -3.11999953e-02\n",
      " -1.33000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.26711867e-01  8.74906048e-02 -4.41063401e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  8.11108923e-02  5.26000047e-02\n",
      "  4.99998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -2.57801827e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -4.39689108e-01  1.16000047e-02\n",
      " -3.00000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -6.00000000e-01  3.88257981e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.20589108e-01  1.86000047e-02\n",
      " -1.67000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  5.26428075e-01 -6.00000000e-01 -6.00000000e-01]\n",
      "Read chunk # 1910 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.81510892e-01  5.06000047e-02\n",
      "  2.07999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -3.09640733e-01  3.95917835e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.75689108e-01 -2.19999953e-02\n",
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      "  3.77033597e-01 -6.00000000e-01  5.96875148e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.93610892e-01 -1.39999953e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -1.09689108e-01 -5.99995308e-04\n",
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      "  2.91705802e-01 -6.00000000e-01 -4.92065244e-01]\n",
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      "  1.79541943e-01 -6.00000000e-01  1.64284899e-01]\n",
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      "  4.70559271e-01  2.53019852e-01  6.00000000e-01]\n",
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      "  6.00000000e-01 -4.74191055e-01  6.00000000e-01]\n",
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      "  6.00000000e-01 -5.19973767e-01  4.80539287e-01]\n",
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      " -6.00000000e-01 -6.00000000e-01 -3.08989723e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  7.96108923e-02  3.00000047e-02\n",
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      "  6.00000000e-01 -2.06420173e-01 -8.77228822e-02]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -4.22589108e-01 -5.93999953e-02\n",
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      "Read chunk # 1941 out of 4999\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.02210892e-01  3.80000469e-03\n",
      "  2.18999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01  8.18392211e-02  3.14575602e-01]\n",
      "Read chunk # 1942 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  4.61089229e-03 -1.79999953e-02\n",
      "  1.61999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.16385804e-01  6.00000000e-01  5.39608749e-01]\n",
      "Read chunk # 1943 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  4.06910892e-01 -4.77999953e-02\n",
      " -1.97000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  5.48319135e-01 -6.00000000e-01  6.00000000e-01]\n",
      "Read chunk # 1944 out of 4999\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.46889108e-01  1.04000047e-02\n",
      " -2.70001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  5.74108923e-02 -1.21999953e-02\n",
      " -1.94000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -5.93240573e-01 -3.10014112e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.33310892e-01 -1.97999953e-02\n",
      " -1.48000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01  3.45494472e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  7.93108923e-02  5.62000047e-02\n",
      "  1.99986580e-04  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.81790547e-01  8.27836339e-04  5.83024789e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.27489108e-01 -1.49999953e-02\n",
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      " -2.90812183e-01  6.00000000e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -7.90891077e-02 -4.35999953e-02\n",
      "  1.42999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.50419365e-01  3.09512300e-01  6.45716628e-03]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.91910892e-01 -3.17999953e-02\n",
      " -1.47000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  3.71089229e-03  1.88000047e-02\n",
      " -5.20001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.97228894e-01  6.00000000e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.77489108e-01  1.88000047e-02\n",
      "  5.19998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -5.58726464e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.15610892e-01 -4.25999953e-02\n",
      " -1.50000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  1.63069663e-01  8.88747140e-02 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  2.38010892e-01  3.92000047e-02\n",
      "  2.50999866e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  5.96021480e-01  6.00000000e-01  4.52034681e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.90789108e-01 -4.83999953e-02\n",
      "  2.29998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -6.00000000e-01  3.90744870e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -3.85089108e-01 -1.79999953e-02\n",
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      " -5.28469616e-01 -6.00000000e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -6.97891077e-02 -5.79999531e-03\n",
      "  8.49998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -1.07713149e-01  6.00000000e-01  7.70650250e-02]\n",
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      "[ 0.00000000e+00  7.95731469e+08  4.06610892e-01 -5.15999953e-02\n",
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      "  3.40621196e-01  3.59749504e-01 -6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.83489108e-01 -8.79999531e-03\n",
      " -7.70001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01 -6.00000000e-01 -4.94585947e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  1.90910892e-01 -4.09999953e-02\n",
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      "  1.93041581e-01 -3.55110418e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.09891077e-02 -3.93999953e-02\n",
      " -2.59000134e-02  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  6.00000000e-01  3.06400912e-01  6.00000000e-01]\n",
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      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  4.75108923e-02 -1.11999953e-02\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([2220000, 6])\n"
     ]
    }
   ],
   "source": [
    "dataset = get_dataset([i for i in range(2000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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': -266.96417,\n",
       " 'std': 124.46084,\n",
       " 'min': -676.4719,\n",
       " 'max': 0.0,\n",
       " 'histogram': (array([  1,   4,  17,  16,  37,  56,  64,  93, 136, 143, 154, 192, 219,\n",
       "         184, 177, 163, 164, 150,  27,   2]),\n",
       "  array([-676.4719  , -642.6483  , -608.8247  , -575.00116 , -541.17755 ,\n",
       "         -507.35394 , -473.53033 , -439.70676 , -405.88315 , -372.05957 ,\n",
       "         -338.23596 , -304.41235 , -270.58878 , -236.76517 , -202.94157 ,\n",
       "         -169.11798 , -135.29439 , -101.47079 ,  -67.647194,  -33.823597,\n",
       "            0.      ], dtype=float32))}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"The behavior policy value statistics are:\")\n",
    "dataset.compute_stats()['return']"
   ]
  },
  {
   "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": 6,
   "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": [
      "0.0751052818464132\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": 8,
   "id": "8f69d169-0f59-4e77-8654-c5d79dae2b1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ERROR: Failed to launch TensorBoard (exited with 1).\n",
       "Contents of stderr:\n",
       "Traceback (most recent call last):\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/bin/tensorboard\", line 6, in <module>\n",
       "    from tensorboard.main import run_main\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/main.py\", line 40, in <module>\n",
       "    from tensorboard import default\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/default.py\", line 38, in <module>\n",
       "    from tensorboard.plugins.audio import audio_plugin\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/plugins/audio/audio_plugin.py\", line 25, in <module>\n",
       "    from tensorboard import plugin_util\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/plugin_util.py\", line 21, in <module>\n",
       "    from tensorboard._vendor import bleach\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/bleach/__init__.py\", line 5, in <module>\n",
       "    from tensorboard._vendor.bleach.linkifier import (\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/bleach/linkifier.py\", line 4, in <module>\n",
       "    from tensorboard._vendor import html5lib\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/__init__.py\", line 25, in <module>\n",
       "    from .html5parser import HTMLParser, parse, parseFragment\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/html5parser.py\", line 8, in <module>\n",
       "    from . import _tokenizer\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/_tokenizer.py\", line 16, in <module>\n",
       "    from ._trie import Trie\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/_trie/__init__.py\", line 3, in <module>\n",
       "    from .py import Trie as PyTrie\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/_trie/py.py\", line 6, in <module>\n",
       "    from ._base import Trie as ABCTrie\n",
       "  File \"/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/tensorboard/_vendor/html5lib/_trie/_base.py\", line 3, in <module>\n",
       "    from collections import Mapping\n",
       "ImportError: cannot import name 'Mapping' from 'collections' (/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/collections/__init__.py)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load_ext tensorboard\n",
    "%tensorboard --logdir runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b0384d5-a843-4d30-a355-404215f27fa4",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 11:50.13 [debug    ] RoundIterator is selected.\n",
      "2022-04-21 11:50.13 [info     ] Directory is created at d3rlpy_logs/CQL_20220421115013\n",
      "2022-04-21 11:50.13 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-21 11:50.13 [debug    ] Fitting reward scaler...       reward_scaler=standard\n",
      "2022-04-21 11:50.13 [warning  ] Skip building models since they're already built.\n",
      "2022-04-21 11:50.13 [info     ] Parameters are saved to d3rlpy_logs/CQL_20220421115013/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': -0.24213339772797096, 'std': 0.14674183337980132, '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": "ab53d1cda56b4221a908ed67cef646f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:00.24 [info     ] CQL_20220421115013: epoch=1 step=6928 epoch=1 metrics={'time_sample_batch': 0.00033248131600042963, 'time_algorithm_update': 0.08607760863271101, 'temp_loss': 3.2389753310668, 'temp': 0.7392859111086187, 'alpha_loss': -13.069082023227462, 'alpha': 1.4248492442604852, 'critic_loss': 22.17708755668261, 'actor_loss': 40.39207670698833, 'time_step': 0.08674688960746034, 'td_error': 16.560567173508446, 'init_value': -82.84752655029297, 'ave_value': -83.19478249575641} step=6928\n",
      "2022-04-21 12:00.24 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_6928.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cccf7b84bcf043c885768b028a0e5ca6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 2/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:11.49 [info     ] CQL_20220421115013: epoch=2 step=13856 epoch=2 metrics={'time_sample_batch': 0.00035565465206912705, 'time_algorithm_update': 0.09660828736445094, 'temp_loss': 0.7820279873513959, 'temp': 0.42524828426281797, 'alpha_loss': -10.031452970154472, 'alpha': 2.767355453334835, 'critic_loss': 54.5907691992458, 'actor_loss': 122.31750482554799, 'time_step': 0.09735213743208737, 'td_error': 44.08262380888173, 'init_value': -162.3415985107422, 'ave_value': -162.9516385855288} step=13856\n",
      "2022-04-21 12:11.49 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_13856.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21fa82552ce54205a25755d67ff54cce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 3/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:22.33 [info     ] CQL_20220421115013: epoch=3 step=20784 epoch=3 metrics={'time_sample_batch': 0.00034207487629412504, 'time_algorithm_update': 0.09070611182454001, 'temp_loss': -0.031370135485415084, 'temp': 0.3827949507768518, 'alpha_loss': -1.6990500891197267, 'alpha': 4.318377103389824, 'critic_loss': 109.7870766075186, 'actor_loss': 201.58327104643365, 'time_step': 0.09143093094126457, 'td_error': 73.65508703299268, 'init_value': -237.88648986816406, 'ave_value': -238.62776678081892} step=20784\n",
      "2022-04-21 12:22.33 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_20784.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c40d089d1984534b99d8043890edb19",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 4/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:34.03 [info     ] CQL_20220421115013: epoch=4 step=27712 epoch=4 metrics={'time_sample_batch': 0.00035602556365191523, 'time_algorithm_update': 0.09727242227652332, 'temp_loss': 0.007291995188477825, 'temp': 0.40115584704517354, 'alpha_loss': 1.4236882650341804, 'alpha': 3.870152088084342, 'critic_loss': 174.9931096444229, 'actor_loss': 269.88629097971574, 'time_step': 0.09802449998761985, 'td_error': 102.32785023277717, 'init_value': -294.13165283203125, 'ave_value': -295.0563412412695} step=27712\n",
      "2022-04-21 12:34.03 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_27712.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d471bd2f6b0b49068afa71ad86e43bcb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 5/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:45.01 [info     ] CQL_20220421115013: epoch=5 step=34640 epoch=5 metrics={'time_sample_batch': 0.0003524545158580049, 'time_algorithm_update': 0.09285861453338252, 'temp_loss': 0.006121914672536784, 'temp': 0.38508266993899404, 'alpha_loss': 0.47146528850017305, 'alpha': 3.302177274543749, 'critic_loss': 226.41000870014724, 'actor_loss': 312.78629176115606, 'time_step': 0.0936035912045155, 'td_error': 125.98956129866822, 'init_value': -324.8154602050781, 'ave_value': -325.92431236631376} step=34640\n",
      "2022-04-21 12:45.01 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_34640.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a552e653c70e407096e9864ec3e0f994",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 6/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 12:55.52 [info     ] CQL_20220421115013: epoch=6 step=41568 epoch=6 metrics={'time_sample_batch': 0.0003440085858726061, 'time_algorithm_update': 0.09165968658191778, 'temp_loss': 0.001111056212601725, 'temp': 0.3771757337955143, 'alpha_loss': 0.2012662655432428, 'alpha': 3.1290670004232375, 'critic_loss': 261.56188339311586, 'actor_loss': 338.74686862690066, 'time_step': 0.09238530062225069, 'td_error': 138.4893357259216, 'init_value': -344.94696044921875, 'ave_value': -346.2934297870945} step=41568\n",
      "2022-04-21 12:55.52 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_41568.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c7290f9a9c9a4db1ab7531f770d59afd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 7/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 13:07.05 [info     ] CQL_20220421115013: epoch=7 step=48496 epoch=7 metrics={'time_sample_batch': 0.0003498173200505986, 'time_algorithm_update': 0.09488137407060583, 'temp_loss': 0.0009336879437382809, 'temp': 0.37271618468828893, 'alpha_loss': 0.1075093630947577, 'alpha': 3.0520785240323107, 'critic_loss': 287.21312825096123, 'actor_loss': 356.10531039601386, 'time_step': 0.09562255528176905, 'td_error': 150.50713562510649, 'init_value': -360.363037109375, 'ave_value': -361.95446174411944} step=48496\n",
      "2022-04-21 13:07.05 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_48496.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac743822cf794f9ea661b98d8e5b2458",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 8/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 13:17.54 [info     ] CQL_20220421115013: epoch=8 step=55424 epoch=8 metrics={'time_sample_batch': 0.00034577483805993414, 'time_algorithm_update': 0.09134256788131546, 'temp_loss': 0.0009413873349982109, 'temp': 0.37095305992088196, 'alpha_loss': 0.14969404571060269, 'alpha': 2.9683321133496854, 'critic_loss': 308.30287659849637, 'actor_loss': 369.25994686716973, 'time_step': 0.09207490275831201, 'td_error': 159.3897181846129, 'init_value': -371.3344421386719, 'ave_value': -373.25395297103745} step=55424\n",
      "2022-04-21 13:17.54 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_55424.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e08f67372754222a22883e37e3da161",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 9/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 13:28.55 [info     ] CQL_20220421115013: epoch=9 step=62352 epoch=9 metrics={'time_sample_batch': 0.00034685935351920185, 'time_algorithm_update': 0.09322074854071091, 'temp_loss': 0.0015006012839263016, 'temp': 0.3692227552664266, 'alpha_loss': 0.10392393775057408, 'alpha': 2.896740374505933, 'critic_loss': 321.75856122801264, 'actor_loss': 377.914640748198, 'time_step': 0.09396069897790428, 'td_error': 165.12380180463168, 'init_value': -377.9372253417969, 'ave_value': -380.1420800606324} step=62352\n",
      "2022-04-21 13:28.55 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_62352.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "495e71d3cb9349bb995e0cf379217da6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 10/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 13:40.35 [info     ] CQL_20220421115013: epoch=10 step=69280 epoch=10 metrics={'time_sample_batch': 0.00035731501309480733, 'time_algorithm_update': 0.09862417746636389, 'temp_loss': 0.0012681218558547622, 'temp': 0.3684202055500575, 'alpha_loss': 0.1515770615696167, 'alpha': 2.8177254600213804, 'critic_loss': 333.2292992143653, 'actor_loss': 385.1046184425266, 'time_step': 0.09938575045203502, 'td_error': 172.1211399006587, 'init_value': -384.555419921875, 'ave_value': -386.9004716346672} step=69280\n",
      "2022-04-21 13:40.35 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_69280.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1484bcd2288b44128165485e08b94250",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 11/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 13:52.10 [info     ] CQL_20220421115013: epoch=11 step=76208 epoch=11 metrics={'time_sample_batch': 0.0003585660567735139, 'time_algorithm_update': 0.09796939298132, 'temp_loss': 1.6746255657882978e-05, 'temp': 0.36547123341375903, 'alpha_loss': 0.03503560832092896, 'alpha': 2.7717734593166767, 'critic_loss': 342.99529306291157, 'actor_loss': 390.85300527929434, 'time_step': 0.09872808147385269, 'td_error': 175.1543666792613, 'init_value': -389.1753845214844, 'ave_value': -391.74348497641625} step=76208\n",
      "2022-04-21 13:52.10 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_76208.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42f5f3d31dfc408f91ae11fcf5854754",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 12/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:03.28 [info     ] CQL_20220421115013: epoch=12 step=83136 epoch=12 metrics={'time_sample_batch': 0.00034692171126535253, 'time_algorithm_update': 0.09554103970114684, 'temp_loss': -0.0009712803984914775, 'temp': 0.36610473489375917, 'alpha_loss': -0.006250801446922763, 'alpha': 2.761431029845055, 'critic_loss': 348.80758486202905, 'actor_loss': 395.52045586918575, 'time_step': 0.09626424013191778, 'td_error': 177.2163282899627, 'init_value': -393.40667724609375, 'ave_value': -396.11357302536834} step=83136\n",
      "2022-04-21 14:03.28 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_83136.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7b49ede4e93c4505a504e892319f0264",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 13/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:14.16 [info     ] CQL_20220421115013: epoch=13 step=90064 epoch=13 metrics={'time_sample_batch': 0.00032177347233884607, 'time_algorithm_update': 0.09150119697111583, 'temp_loss': -0.0009721430900665839, 'temp': 0.36759291317821513, 'alpha_loss': 0.10278147696235192, 'alpha': 2.7262383962338297, 'critic_loss': 350.300065931492, 'actor_loss': 397.83247016483864, 'time_step': 0.0921649882617228, 'td_error': 176.49191251676035, 'init_value': -393.9203796386719, 'ave_value': -396.94941175000304} step=90064\n",
      "2022-04-21 14:14.16 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_90064.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "440cbe31f1374b20bb65fdc748d9d882",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 14/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:25.09 [info     ] CQL_20220421115013: epoch=14 step=96992 epoch=14 metrics={'time_sample_batch': 0.0003249383344385971, 'time_algorithm_update': 0.09214452662726748, 'temp_loss': 0.0038507886726786137, 'temp': 0.3662289146282312, 'alpha_loss': -0.022483001650088843, 'alpha': 2.70475452997255, 'critic_loss': 348.53530670616425, 'actor_loss': 398.7760192747755, 'time_step': 0.0928169010899634, 'td_error': 174.83922750364198, 'init_value': -394.4867248535156, 'ave_value': -397.6879507944949} step=96992\n",
      "2022-04-21 14:25.09 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_96992.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14f86fb108c646a3bdc3c0171740b57e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 15/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:35.58 [info     ] CQL_20220421115013: epoch=15 step=103920 epoch=15 metrics={'time_sample_batch': 0.00032353036398149803, 'time_algorithm_update': 0.09161030485641047, 'temp_loss': -0.002446396822202843, 'temp': 0.3649185357430378, 'alpha_loss': 0.0963416375972976, 'alpha': 2.684158604187723, 'critic_loss': 345.85602966976495, 'actor_loss': 398.6164425019579, 'time_step': 0.09227670863374, 'td_error': 173.41514014054846, 'init_value': -393.3376159667969, 'ave_value': -396.95405322141903} step=103920\n",
      "2022-04-21 14:35.58 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_103920.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8349da5b553a42f780bba1f5b195d8f9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 16/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:46.51 [info     ] CQL_20220421115013: epoch=16 step=110848 epoch=16 metrics={'time_sample_batch': 0.00032688281552620903, 'time_algorithm_update': 0.09209509551800427, 'temp_loss': 0.0008673044141716975, 'temp': 0.36439287055963454, 'alpha_loss': 0.08642494658226715, 'alpha': 2.632425872165942, 'critic_loss': 342.2782390092179, 'actor_loss': 397.2269722848236, 'time_step': 0.092767263945476, 'td_error': 172.1287611125964, 'init_value': -392.37652587890625, 'ave_value': -396.310009416185} step=110848\n",
      "2022-04-21 14:46.51 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_110848.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a23178c23b844d50b8d5851b1e388586",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 17/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 14:57.55 [info     ] CQL_20220421115013: epoch=17 step=117776 epoch=17 metrics={'time_sample_batch': 0.0003239320070583881, 'time_algorithm_update': 0.09370522135675091, 'temp_loss': 0.0006810816209002422, 'temp': 0.3629982714685364, 'alpha_loss': 0.03625618569291115, 'alpha': 2.5694400906287496, 'critic_loss': 339.46451183857596, 'actor_loss': 396.2262317269834, 'time_step': 0.09437147667308877, 'td_error': 169.1923493781587, 'init_value': -386.9462585449219, 'ave_value': -391.41588389656135} step=117776\n",
      "2022-04-21 14:57.55 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_117776.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9b79d943b7434da3b0aa4a652f1730d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 18/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 15:09.05 [info     ] CQL_20220421115013: epoch=18 step=124704 epoch=18 metrics={'time_sample_batch': 0.0003292814895132122, 'time_algorithm_update': 0.09451565308190933, 'temp_loss': 0.0006029767892148677, 'temp': 0.36350554710912236, 'alpha_loss': 0.054510936099135335, 'alpha': 2.5250505440573767, 'critic_loss': 333.70101388745167, 'actor_loss': 393.90564168353006, 'time_step': 0.09519228270367718, 'td_error': 166.65178552178062, 'init_value': -386.75897216796875, 'ave_value': -391.73727577085754} step=124704\n",
      "2022-04-21 15:09.05 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_124704.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a4dd1a0f1104115a4eba6cfbe2b2d80",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 19/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 15:19.55 [info     ] CQL_20220421115013: epoch=19 step=131632 epoch=19 metrics={'time_sample_batch': 0.00032722857064518035, 'time_algorithm_update': 0.09183631651533669, 'temp_loss': -0.0003157147547747944, 'temp': 0.3656944708412569, 'alpha_loss': 0.11574303269185976, 'alpha': 2.48160609984921, 'critic_loss': 327.86961024166806, 'actor_loss': 391.5019153744739, 'time_step': 0.09250937312772588, 'td_error': 164.24851129191117, 'init_value': -383.9179992675781, 'ave_value': -389.5521777916642} step=131632\n",
      "2022-04-21 15:19.55 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_131632.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "201ea9a21084423ea20bc8bc6a6ac065",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 20/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 15:30.50 [info     ] CQL_20220421115013: epoch=20 step=138560 epoch=20 metrics={'time_sample_batch': 0.0003276263249663778, 'time_algorithm_update': 0.09253235932172858, 'temp_loss': -0.000803443935323898, 'temp': 0.36405753765435883, 'alpha_loss': 0.07446995459914342, 'alpha': 2.4372971633290033, 'critic_loss': 324.1151957347404, 'actor_loss': 390.28327567913243, 'time_step': 0.09320770847604677, 'td_error': 161.1472516706148, 'init_value': -383.5263366699219, 'ave_value': -389.4972804302525} step=138560\n",
      "2022-04-21 15:30.50 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_138560.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "11d8dc5ce79645f59f407408be27ac10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 21/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 15:41.37 [info     ] CQL_20220421115013: epoch=21 step=145488 epoch=21 metrics={'time_sample_batch': 0.00032325918349603033, 'time_algorithm_update': 0.09138222761434991, 'temp_loss': 0.0023087718405136537, 'temp': 0.36506183394693337, 'alpha_loss': 0.07145465938295928, 'alpha': 2.3780580226323345, 'critic_loss': 317.14554573805447, 'actor_loss': 388.71824369804966, 'time_step': 0.09205060460826266, 'td_error': 156.21058388132695, 'init_value': -379.9534912109375, 'ave_value': -386.17341501469656} step=145488\n",
      "2022-04-21 15:41.37 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_145488.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "874de4aadb534a9e880584a729e35445",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 22/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 15:52.14 [info     ] CQL_20220421115013: epoch=22 step=152416 epoch=22 metrics={'time_sample_batch': 0.0003211216755882422, 'time_algorithm_update': 0.09031135914094454, 'temp_loss': -0.0006649349533843079, 'temp': 0.36311562696157784, 'alpha_loss': 0.06671501420333334, 'alpha': 2.343121531963624, 'critic_loss': 310.42988414590957, 'actor_loss': 386.7907238931634, 'time_step': 0.09097466388971095, 'td_error': 156.54176283794678, 'init_value': -380.8991394042969, 'ave_value': -387.2388916392971} step=152416\n",
      "2022-04-21 15:52.14 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_152416.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9c8e2f259e5a480bbdc337fc1558b370",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 23/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:03.12 [info     ] CQL_20220421115013: epoch=23 step=159344 epoch=23 metrics={'time_sample_batch': 0.00032859042665424305, 'time_algorithm_update': 0.0929140043176074, 'temp_loss': -0.0006962519806288678, 'temp': 0.36287562139160223, 'alpha_loss': 0.07117197230760489, 'alpha': 2.3050500192694523, 'critic_loss': 307.8786829575793, 'actor_loss': 385.1747925562341, 'time_step': 0.09359419893034611, 'td_error': 152.89913267123512, 'init_value': -378.15374755859375, 'ave_value': -384.55256071310646} step=159344\n",
      "2022-04-21 16:03.12 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_159344.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5cd18fc668a54ef7a3380ec035c65368",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 24/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:14.00 [info     ] CQL_20220421115013: epoch=24 step=166272 epoch=24 metrics={'time_sample_batch': 0.0003260050923939941, 'time_algorithm_update': 0.0915383514507263, 'temp_loss': -0.00013338399147770774, 'temp': 0.36235369468327205, 'alpha_loss': 0.059480640033294806, 'alpha': 2.279450998742641, 'critic_loss': 305.45263398801757, 'actor_loss': 383.6212434614503, 'time_step': 0.09220948853200908, 'td_error': 153.7780558300682, 'init_value': -376.93389892578125, 'ave_value': -383.46135572522826} step=166272\n",
      "2022-04-21 16:14.00 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_166272.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "287a8c1af2f54e0b8ba7cbcc7325e14b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 25/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:24.45 [info     ] CQL_20220421115013: epoch=25 step=173200 epoch=25 metrics={'time_sample_batch': 0.00032344050963536145, 'time_algorithm_update': 0.0909941667910261, 'temp_loss': 0.0010177633871230798, 'temp': 0.3617817800552272, 'alpha_loss': -0.015428371302688449, 'alpha': 2.249002670546602, 'critic_loss': 305.4925143580376, 'actor_loss': 382.35815847277917, 'time_step': 0.09166323983779405, 'td_error': 151.9830163939512, 'init_value': -372.828125, 'ave_value': -379.50794296732033} step=173200\n",
      "2022-04-21 16:24.45 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_173200.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f38b3b9b41f045b38e51f9181a368e59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 26/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:35.27 [info     ] CQL_20220421115013: epoch=26 step=180128 epoch=26 metrics={'time_sample_batch': 0.0003240840126680722, 'time_algorithm_update': 0.0907728812435758, 'temp_loss': 0.0012598659248829562, 'temp': 0.3618432446866883, 'alpha_loss': 0.05611779912422367, 'alpha': 2.232187041303027, 'critic_loss': 305.37339683809677, 'actor_loss': 381.0610024934553, 'time_step': 0.09144208100183455, 'td_error': 151.055012673217, 'init_value': -371.3172912597656, 'ave_value': -378.1840906400251} step=180128\n",
      "2022-04-21 16:35.27 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_180128.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "814a64532bf04893a2ca46305afa2c33",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 27/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:46.06 [info     ] CQL_20220421115013: epoch=27 step=187056 epoch=27 metrics={'time_sample_batch': 0.00032276747959040055, 'time_algorithm_update': 0.09020295370946581, 'temp_loss': -0.000996722697547054, 'temp': 0.3611607913779607, 'alpha_loss': 0.02553116674774932, 'alpha': 2.199727045780792, 'critic_loss': 303.8417127645181, 'actor_loss': 379.27491507409076, 'time_step': 0.0908678944887245, 'td_error': 151.1714805092278, 'init_value': -372.2703857421875, 'ave_value': -379.3775645807627} step=187056\n",
      "2022-04-21 16:46.06 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_187056.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "92dbc5875a48445fb246df9af056879b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 28/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 16:56.47 [info     ] CQL_20220421115013: epoch=28 step=193984 epoch=28 metrics={'time_sample_batch': 0.00032247547877578206, 'time_algorithm_update': 0.0905953502889043, 'temp_loss': -0.002661704557881138, 'temp': 0.3603977707083451, 'alpha_loss': 0.042370417688789214, 'alpha': 2.183184081907911, 'critic_loss': 303.5562005534458, 'actor_loss': 377.9231832649636, 'time_step': 0.09126210563727945, 'td_error': 150.64589724695094, 'init_value': -369.5162353515625, 'ave_value': -376.9608241176777} step=193984\n",
      "2022-04-21 16:56.47 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_193984.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9f8c0cf0e7bf4425a137947396fb4cd8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 29/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 17:07.22 [info     ] CQL_20220421115013: epoch=29 step=200912 epoch=29 metrics={'time_sample_batch': 0.00032239536352576066, 'time_algorithm_update': 0.08971680284372378, 'temp_loss': 0.0030905455615364476, 'temp': 0.36107396472925124, 'alpha_loss': 0.049012036066280594, 'alpha': 2.150564372401865, 'critic_loss': 303.9037359236432, 'actor_loss': 377.110741952275, 'time_step': 0.09038400519252099, 'td_error': 152.11518715758464, 'init_value': -367.2611083984375, 'ave_value': -375.005281074438} step=200912\n",
      "2022-04-21 17:07.22 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_200912.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1e907b21ce0c44c585465d60eca81d03",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 30/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 17:18.11 [info     ] CQL_20220421115013: epoch=30 step=207840 epoch=30 metrics={'time_sample_batch': 0.0003254886794035209, 'time_algorithm_update': 0.09166120126946693, 'temp_loss': -0.0005623962813918425, 'temp': 0.3596801210934722, 'alpha_loss': -0.004097521271960137, 'alpha': 2.143575791433006, 'critic_loss': 303.5697760577152, 'actor_loss': 375.7562272113685, 'time_step': 0.09233160406419917, 'td_error': 151.80717670504805, 'init_value': -366.3521728515625, 'ave_value': -374.31117526765775} step=207840\n",
      "2022-04-21 17:18.11 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_207840.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a75dfbabf444e33a363892e50c07cc8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 31/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 17:28.45 [info     ] CQL_20220421115013: epoch=31 step=214768 epoch=31 metrics={'time_sample_batch': 0.0003232552259128033, 'time_algorithm_update': 0.0894827664793226, 'temp_loss': 0.00012451626421447078, 'temp': 0.36164807951116534, 'alpha_loss': 0.053641710409832885, 'alpha': 2.126187172518316, 'critic_loss': 301.85631993123894, 'actor_loss': 373.8286955307042, 'time_step': 0.09015002082732203, 'td_error': 149.9945924684128, 'init_value': -364.87469482421875, 'ave_value': -373.04969961284945} step=214768\n",
      "2022-04-21 17:28.45 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_214768.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aef38fe9fb854c96a2512615a201328a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 32/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 17:39.25 [info     ] CQL_20220421115013: epoch=32 step=221696 epoch=32 metrics={'time_sample_batch': 0.0003223392002577044, 'time_algorithm_update': 0.09044677290988024, 'temp_loss': 0.0016147525276056884, 'temp': 0.3578099250991336, 'alpha_loss': 0.03625635007326538, 'alpha': 2.090304651037375, 'critic_loss': 300.0765135741812, 'actor_loss': 371.7944967212633, 'time_step': 0.09111578439041869, 'td_error': 148.13623125843387, 'init_value': -361.79571533203125, 'ave_value': -370.2364439860851} step=221696\n",
      "2022-04-21 17:39.25 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_221696.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a14d645d0eb4359a7978440d3164617",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 17:50.03 [info     ] CQL_20220421115013: epoch=33 step=228624 epoch=33 metrics={'time_sample_batch': 0.0003229059605895785, 'time_algorithm_update': 0.09017169568472591, 'temp_loss': 0.00011560220318264597, 'temp': 0.3566744191653803, 'alpha_loss': 0.010538321456203615, 'alpha': 2.0762205004829735, 'critic_loss': 296.5471138163334, 'actor_loss': 368.3070477756562, 'time_step': 0.09084160075314326, 'td_error': 146.4533405497028, 'init_value': -356.5564880371094, 'ave_value': -365.1387829347559} step=228624\n",
      "2022-04-21 17:50.03 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_228624.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "abb184b82bdb497ea5c0bf0c8975bdaa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 34/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:00.44 [info     ] CQL_20220421115013: epoch=34 step=235552 epoch=34 metrics={'time_sample_batch': 0.0003215058020576318, 'time_algorithm_update': 0.09044981925096578, 'temp_loss': -0.0010950191072285106, 'temp': 0.35856941925667174, 'alpha_loss': 0.04142675211807505, 'alpha': 2.071470718013497, 'critic_loss': 291.8196984599699, 'actor_loss': 364.3278335016264, 'time_step': 0.09111290650472355, 'td_error': 145.6282216482493, 'init_value': -352.9852600097656, 'ave_value': -361.8486650639182} step=235552\n",
      "2022-04-21 18:00.44 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_235552.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9911593f87c744dcab127cb191378146",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 35/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:11.30 [info     ] CQL_20220421115013: epoch=35 step=242480 epoch=35 metrics={'time_sample_batch': 0.00032443444805938155, 'time_algorithm_update': 0.09131960811708596, 'temp_loss': -0.0015185632508430376, 'temp': 0.3589464257671293, 'alpha_loss': 0.014045644557186793, 'alpha': 2.0533931623567887, 'critic_loss': 286.35663724558185, 'actor_loss': 360.7822869897715, 'time_step': 0.09199312780112648, 'td_error': 141.00195396904383, 'init_value': -348.13836669921875, 'ave_value': -356.93436600269285} step=242480\n",
      "2022-04-21 18:11.30 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_242480.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5baa72f2907e4e91a941888a0108094e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 36/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:22.16 [info     ] CQL_20220421115013: epoch=36 step=249408 epoch=36 metrics={'time_sample_batch': 0.0003249190971427371, 'time_algorithm_update': 0.0910941548559462, 'temp_loss': 0.00042742281694714774, 'temp': 0.35977009587058706, 'alpha_loss': -0.013534218509742756, 'alpha': 2.0500835082547493, 'critic_loss': 281.40552348146264, 'actor_loss': 356.75915294761745, 'time_step': 0.0917667726928037, 'td_error': 138.4451647975536, 'init_value': -343.50421142578125, 'ave_value': -352.4618402976861} step=249408\n",
      "2022-04-21 18:22.16 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_249408.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "19a26085b15448098d180e346acff2b8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 37/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:32.52 [info     ] CQL_20220421115013: epoch=37 step=256336 epoch=37 metrics={'time_sample_batch': 0.00032253676969514985, 'time_algorithm_update': 0.08986017923569845, 'temp_loss': -0.0010712851957951975, 'temp': 0.360286415616958, 'alpha_loss': -0.01475848322492601, 'alpha': 2.0511225345641306, 'critic_loss': 278.27052969139663, 'actor_loss': 354.3621027640327, 'time_step': 0.0905305280008162, 'td_error': 137.7702907378289, 'init_value': -343.20147705078125, 'ave_value': -352.1003512276787} step=256336\n",
      "2022-04-21 18:32.52 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_256336.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cce26afb7a61404491d77e8f9438ce27",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 38/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:43.42 [info     ] CQL_20220421115013: epoch=38 step=263264 epoch=38 metrics={'time_sample_batch': 0.0003248480671272542, 'time_algorithm_update': 0.09184583395237736, 'temp_loss': 0.00012206523104177736, 'temp': 0.36190666509985786, 'alpha_loss': 0.0034238402221727455, 'alpha': 2.044888884723875, 'critic_loss': 275.5247867621533, 'actor_loss': 352.3196665343181, 'time_step': 0.0925158032024705, 'td_error': 137.03004360076602, 'init_value': -341.3427429199219, 'ave_value': -350.2393808206395} step=263264\n",
      "2022-04-21 18:43.42 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_263264.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d676aff6c0d9434ea7524e2c78595a2e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 39/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 18:54.21 [info     ] CQL_20220421115013: epoch=39 step=270192 epoch=39 metrics={'time_sample_batch': 0.0003210365359282108, 'time_algorithm_update': 0.09014628548820354, 'temp_loss': 0.0022400938514923873, 'temp': 0.36381051433433975, 'alpha_loss': 0.04880675139081679, 'alpha': 2.0301015755596117, 'critic_loss': 274.2025833403128, 'actor_loss': 350.8632940199854, 'time_step': 0.09081239499340982, 'td_error': 137.92932967987878, 'init_value': -341.0992126464844, 'ave_value': -349.7714659517542} step=270192\n",
      "2022-04-21 18:54.21 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_270192.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "57b1096810a14e6ea8f45a2c9a25ef13",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 40/40:   0%|          | 0/6928 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-21 19:05.06 [info     ] CQL_20220421115013: epoch=40 step=277120 epoch=40 metrics={'time_sample_batch': 0.0003227231546582581, 'time_algorithm_update': 0.09107090109490357, 'temp_loss': -0.0017881930465167431, 'temp': 0.3647903068675111, 'alpha_loss': 0.063628239421584, 'alpha': 2.020732377799912, 'critic_loss': 273.50766204769013, 'actor_loss': 350.067744420252, 'time_step': 0.09173897290752886, 'td_error': 134.35094199225546, 'init_value': -337.4706115722656, 'ave_value': -346.0951132238864} step=277120\n",
      "2022-04-21 19:05.06 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220421115013/model_277120.pt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(1,\n",
       "  {'time_sample_batch': 0.00033248131600042963,\n",
       "   'time_algorithm_update': 0.08607760863271101,\n",
       "   'temp_loss': 3.2389753310668,\n",
       "   'temp': 0.7392859111086187,\n",
       "   'alpha_loss': -13.069082023227462,\n",
       "   'alpha': 1.4248492442604852,\n",
       "   'critic_loss': 22.17708755668261,\n",
       "   'actor_loss': 40.39207670698833,\n",
       "   'time_step': 0.08674688960746034,\n",
       "   'td_error': 16.560567173508446,\n",
       "   'init_value': -82.84752655029297,\n",
       "   'ave_value': -83.19478249575641}),\n",
       " (2,\n",
       "  {'time_sample_batch': 0.00035565465206912705,\n",
       "   'time_algorithm_update': 0.09660828736445094,\n",
       "   'temp_loss': 0.7820279873513959,\n",
       "   'temp': 0.42524828426281797,\n",
       "   'alpha_loss': -10.031452970154472,\n",
       "   'alpha': 2.767355453334835,\n",
       "   'critic_loss': 54.5907691992458,\n",
       "   'actor_loss': 122.31750482554799,\n",
       "   'time_step': 0.09735213743208737,\n",
       "   'td_error': 44.08262380888173,\n",
       "   'init_value': -162.3415985107422,\n",
       "   'ave_value': -162.9516385855288}),\n",
       " (3,\n",
       "  {'time_sample_batch': 0.00034207487629412504,\n",
       "   'time_algorithm_update': 0.09070611182454001,\n",
       "   'temp_loss': -0.031370135485415084,\n",
       "   'temp': 0.3827949507768518,\n",
       "   'alpha_loss': -1.6990500891197267,\n",
       "   'alpha': 4.318377103389824,\n",
       "   'critic_loss': 109.7870766075186,\n",
       "   'actor_loss': 201.58327104643365,\n",
       "   'time_step': 0.09143093094126457,\n",
       "   'td_error': 73.65508703299268,\n",
       "   'init_value': -237.88648986816406,\n",
       "   'ave_value': -238.62776678081892}),\n",
       " (4,\n",
       "  {'time_sample_batch': 0.00035602556365191523,\n",
       "   'time_algorithm_update': 0.09727242227652332,\n",
       "   'temp_loss': 0.007291995188477825,\n",
       "   'temp': 0.40115584704517354,\n",
       "   'alpha_loss': 1.4236882650341804,\n",
       "   'alpha': 3.870152088084342,\n",
       "   'critic_loss': 174.9931096444229,\n",
       "   'actor_loss': 269.88629097971574,\n",
       "   'time_step': 0.09802449998761985,\n",
       "   'td_error': 102.32785023277717,\n",
       "   'init_value': -294.13165283203125,\n",
       "   'ave_value': -295.0563412412695}),\n",
       " (5,\n",
       "  {'time_sample_batch': 0.0003524545158580049,\n",
       "   'time_algorithm_update': 0.09285861453338252,\n",
       "   'temp_loss': 0.006121914672536784,\n",
       "   'temp': 0.38508266993899404,\n",
       "   'alpha_loss': 0.47146528850017305,\n",
       "   'alpha': 3.302177274543749,\n",
       "   'critic_loss': 226.41000870014724,\n",
       "   'actor_loss': 312.78629176115606,\n",
       "   'time_step': 0.0936035912045155,\n",
       "   'td_error': 125.98956129866822,\n",
       "   'init_value': -324.8154602050781,\n",
       "   'ave_value': -325.92431236631376}),\n",
       " (6,\n",
       "  {'time_sample_batch': 0.0003440085858726061,\n",
       "   'time_algorithm_update': 0.09165968658191778,\n",
       "   'temp_loss': 0.001111056212601725,\n",
       "   'temp': 0.3771757337955143,\n",
       "   'alpha_loss': 0.2012662655432428,\n",
       "   'alpha': 3.1290670004232375,\n",
       "   'critic_loss': 261.56188339311586,\n",
       "   'actor_loss': 338.74686862690066,\n",
       "   'time_step': 0.09238530062225069,\n",
       "   'td_error': 138.4893357259216,\n",
       "   'init_value': -344.94696044921875,\n",
       "   'ave_value': -346.2934297870945}),\n",
       " (7,\n",
       "  {'time_sample_batch': 0.0003498173200505986,\n",
       "   'time_algorithm_update': 0.09488137407060583,\n",
       "   'temp_loss': 0.0009336879437382809,\n",
       "   'temp': 0.37271618468828893,\n",
       "   'alpha_loss': 0.1075093630947577,\n",
       "   'alpha': 3.0520785240323107,\n",
       "   'critic_loss': 287.21312825096123,\n",
       "   'actor_loss': 356.10531039601386,\n",
       "   'time_step': 0.09562255528176905,\n",
       "   'td_error': 150.50713562510649,\n",
       "   'init_value': -360.363037109375,\n",
       "   'ave_value': -361.95446174411944}),\n",
       " (8,\n",
       "  {'time_sample_batch': 0.00034577483805993414,\n",
       "   'time_algorithm_update': 0.09134256788131546,\n",
       "   'temp_loss': 0.0009413873349982109,\n",
       "   'temp': 0.37095305992088196,\n",
       "   'alpha_loss': 0.14969404571060269,\n",
       "   'alpha': 2.9683321133496854,\n",
       "   'critic_loss': 308.30287659849637,\n",
       "   'actor_loss': 369.25994686716973,\n",
       "   'time_step': 0.09207490275831201,\n",
       "   'td_error': 159.3897181846129,\n",
       "   'init_value': -371.3344421386719,\n",
       "   'ave_value': -373.25395297103745}),\n",
       " (9,\n",
       "  {'time_sample_batch': 0.00034685935351920185,\n",
       "   'time_algorithm_update': 0.09322074854071091,\n",
       "   'temp_loss': 0.0015006012839263016,\n",
       "   'temp': 0.3692227552664266,\n",
       "   'alpha_loss': 0.10392393775057408,\n",
       "   'alpha': 2.896740374505933,\n",
       "   'critic_loss': 321.75856122801264,\n",
       "   'actor_loss': 377.914640748198,\n",
       "   'time_step': 0.09396069897790428,\n",
       "   'td_error': 165.12380180463168,\n",
       "   'init_value': -377.9372253417969,\n",
       "   'ave_value': -380.1420800606324}),\n",
       " (10,\n",
       "  {'time_sample_batch': 0.00035731501309480733,\n",
       "   'time_algorithm_update': 0.09862417746636389,\n",
       "   'temp_loss': 0.0012681218558547622,\n",
       "   'temp': 0.3684202055500575,\n",
       "   'alpha_loss': 0.1515770615696167,\n",
       "   'alpha': 2.8177254600213804,\n",
       "   'critic_loss': 333.2292992143653,\n",
       "   'actor_loss': 385.1046184425266,\n",
       "   'time_step': 0.09938575045203502,\n",
       "   'td_error': 172.1211399006587,\n",
       "   'init_value': -384.555419921875,\n",
       "   'ave_value': -386.9004716346672}),\n",
       " (11,\n",
       "  {'time_sample_batch': 0.0003585660567735139,\n",
       "   'time_algorithm_update': 0.09796939298132,\n",
       "   'temp_loss': 1.6746255657882978e-05,\n",
       "   'temp': 0.36547123341375903,\n",
       "   'alpha_loss': 0.03503560832092896,\n",
       "   'alpha': 2.7717734593166767,\n",
       "   'critic_loss': 342.99529306291157,\n",
       "   'actor_loss': 390.85300527929434,\n",
       "   'time_step': 0.09872808147385269,\n",
       "   'td_error': 175.1543666792613,\n",
       "   'init_value': -389.1753845214844,\n",
       "   'ave_value': -391.74348497641625}),\n",
       " (12,\n",
       "  {'time_sample_batch': 0.00034692171126535253,\n",
       "   'time_algorithm_update': 0.09554103970114684,\n",
       "   'temp_loss': -0.0009712803984914775,\n",
       "   'temp': 0.36610473489375917,\n",
       "   'alpha_loss': -0.006250801446922763,\n",
       "   'alpha': 2.761431029845055,\n",
       "   'critic_loss': 348.80758486202905,\n",
       "   'actor_loss': 395.52045586918575,\n",
       "   'time_step': 0.09626424013191778,\n",
       "   'td_error': 177.2163282899627,\n",
       "   'init_value': -393.40667724609375,\n",
       "   'ave_value': -396.11357302536834}),\n",
       " (13,\n",
       "  {'time_sample_batch': 0.00032177347233884607,\n",
       "   'time_algorithm_update': 0.09150119697111583,\n",
       "   'temp_loss': -0.0009721430900665839,\n",
       "   'temp': 0.36759291317821513,\n",
       "   'alpha_loss': 0.10278147696235192,\n",
       "   'alpha': 2.7262383962338297,\n",
       "   'critic_loss': 350.300065931492,\n",
       "   'actor_loss': 397.83247016483864,\n",
       "   'time_step': 0.0921649882617228,\n",
       "   'td_error': 176.49191251676035,\n",
       "   'init_value': -393.9203796386719,\n",
       "   'ave_value': -396.94941175000304}),\n",
       " (14,\n",
       "  {'time_sample_batch': 0.0003249383344385971,\n",
       "   'time_algorithm_update': 0.09214452662726748,\n",
       "   'temp_loss': 0.0038507886726786137,\n",
       "   'temp': 0.3662289146282312,\n",
       "   'alpha_loss': -0.022483001650088843,\n",
       "   'alpha': 2.70475452997255,\n",
       "   'critic_loss': 348.53530670616425,\n",
       "   'actor_loss': 398.7760192747755,\n",
       "   'time_step': 0.0928169010899634,\n",
       "   'td_error': 174.83922750364198,\n",
       "   'init_value': -394.4867248535156,\n",
       "   'ave_value': -397.6879507944949}),\n",
       " (15,\n",
       "  {'time_sample_batch': 0.00032353036398149803,\n",
       "   'time_algorithm_update': 0.09161030485641047,\n",
       "   'temp_loss': -0.002446396822202843,\n",
       "   'temp': 0.3649185357430378,\n",
       "   'alpha_loss': 0.0963416375972976,\n",
       "   'alpha': 2.684158604187723,\n",
       "   'critic_loss': 345.85602966976495,\n",
       "   'actor_loss': 398.6164425019579,\n",
       "   'time_step': 0.09227670863374,\n",
       "   'td_error': 173.41514014054846,\n",
       "   'init_value': -393.3376159667969,\n",
       "   'ave_value': -396.95405322141903}),\n",
       " (16,\n",
       "  {'time_sample_batch': 0.00032688281552620903,\n",
       "   'time_algorithm_update': 0.09209509551800427,\n",
       "   'temp_loss': 0.0008673044141716975,\n",
       "   'temp': 0.36439287055963454,\n",
       "   'alpha_loss': 0.08642494658226715,\n",
       "   'alpha': 2.632425872165942,\n",
       "   'critic_loss': 342.2782390092179,\n",
       "   'actor_loss': 397.2269722848236,\n",
       "   'time_step': 0.092767263945476,\n",
       "   'td_error': 172.1287611125964,\n",
       "   'init_value': -392.37652587890625,\n",
       "   'ave_value': -396.310009416185}),\n",
       " (17,\n",
       "  {'time_sample_batch': 0.0003239320070583881,\n",
       "   'time_algorithm_update': 0.09370522135675091,\n",
       "   'temp_loss': 0.0006810816209002422,\n",
       "   'temp': 0.3629982714685364,\n",
       "   'alpha_loss': 0.03625618569291115,\n",
       "   'alpha': 2.5694400906287496,\n",
       "   'critic_loss': 339.46451183857596,\n",
       "   'actor_loss': 396.2262317269834,\n",
       "   'time_step': 0.09437147667308877,\n",
       "   'td_error': 169.1923493781587,\n",
       "   'init_value': -386.9462585449219,\n",
       "   'ave_value': -391.41588389656135}),\n",
       " (18,\n",
       "  {'time_sample_batch': 0.0003292814895132122,\n",
       "   'time_algorithm_update': 0.09451565308190933,\n",
       "   'temp_loss': 0.0006029767892148677,\n",
       "   'temp': 0.36350554710912236,\n",
       "   'alpha_loss': 0.054510936099135335,\n",
       "   'alpha': 2.5250505440573767,\n",
       "   'critic_loss': 333.70101388745167,\n",
       "   'actor_loss': 393.90564168353006,\n",
       "   'time_step': 0.09519228270367718,\n",
       "   'td_error': 166.65178552178062,\n",
       "   'init_value': -386.75897216796875,\n",
       "   'ave_value': -391.73727577085754}),\n",
       " (19,\n",
       "  {'time_sample_batch': 0.00032722857064518035,\n",
       "   'time_algorithm_update': 0.09183631651533669,\n",
       "   'temp_loss': -0.0003157147547747944,\n",
       "   'temp': 0.3656944708412569,\n",
       "   'alpha_loss': 0.11574303269185976,\n",
       "   'alpha': 2.48160609984921,\n",
       "   'critic_loss': 327.86961024166806,\n",
       "   'actor_loss': 391.5019153744739,\n",
       "   'time_step': 0.09250937312772588,\n",
       "   'td_error': 164.24851129191117,\n",
       "   'init_value': -383.9179992675781,\n",
       "   'ave_value': -389.5521777916642}),\n",
       " (20,\n",
       "  {'time_sample_batch': 0.0003276263249663778,\n",
       "   'time_algorithm_update': 0.09253235932172858,\n",
       "   'temp_loss': -0.000803443935323898,\n",
       "   'temp': 0.36405753765435883,\n",
       "   'alpha_loss': 0.07446995459914342,\n",
       "   'alpha': 2.4372971633290033,\n",
       "   'critic_loss': 324.1151957347404,\n",
       "   'actor_loss': 390.28327567913243,\n",
       "   'time_step': 0.09320770847604677,\n",
       "   'td_error': 161.1472516706148,\n",
       "   'init_value': -383.5263366699219,\n",
       "   'ave_value': -389.4972804302525}),\n",
       " (21,\n",
       "  {'time_sample_batch': 0.00032325918349603033,\n",
       "   'time_algorithm_update': 0.09138222761434991,\n",
       "   'temp_loss': 0.0023087718405136537,\n",
       "   'temp': 0.36506183394693337,\n",
       "   'alpha_loss': 0.07145465938295928,\n",
       "   'alpha': 2.3780580226323345,\n",
       "   'critic_loss': 317.14554573805447,\n",
       "   'actor_loss': 388.71824369804966,\n",
       "   'time_step': 0.09205060460826266,\n",
       "   'td_error': 156.21058388132695,\n",
       "   'init_value': -379.9534912109375,\n",
       "   'ave_value': -386.17341501469656}),\n",
       " (22,\n",
       "  {'time_sample_batch': 0.0003211216755882422,\n",
       "   'time_algorithm_update': 0.09031135914094454,\n",
       "   'temp_loss': -0.0006649349533843079,\n",
       "   'temp': 0.36311562696157784,\n",
       "   'alpha_loss': 0.06671501420333334,\n",
       "   'alpha': 2.343121531963624,\n",
       "   'critic_loss': 310.42988414590957,\n",
       "   'actor_loss': 386.7907238931634,\n",
       "   'time_step': 0.09097466388971095,\n",
       "   'td_error': 156.54176283794678,\n",
       "   'init_value': -380.8991394042969,\n",
       "   'ave_value': -387.2388916392971}),\n",
       " (23,\n",
       "  {'time_sample_batch': 0.00032859042665424305,\n",
       "   'time_algorithm_update': 0.0929140043176074,\n",
       "   'temp_loss': -0.0006962519806288678,\n",
       "   'temp': 0.36287562139160223,\n",
       "   'alpha_loss': 0.07117197230760489,\n",
       "   'alpha': 2.3050500192694523,\n",
       "   'critic_loss': 307.8786829575793,\n",
       "   'actor_loss': 385.1747925562341,\n",
       "   'time_step': 0.09359419893034611,\n",
       "   'td_error': 152.89913267123512,\n",
       "   'init_value': -378.15374755859375,\n",
       "   'ave_value': -384.55256071310646}),\n",
       " (24,\n",
       "  {'time_sample_batch': 0.0003260050923939941,\n",
       "   'time_algorithm_update': 0.0915383514507263,\n",
       "   'temp_loss': -0.00013338399147770774,\n",
       "   'temp': 0.36235369468327205,\n",
       "   'alpha_loss': 0.059480640033294806,\n",
       "   'alpha': 2.279450998742641,\n",
       "   'critic_loss': 305.45263398801757,\n",
       "   'actor_loss': 383.6212434614503,\n",
       "   'time_step': 0.09220948853200908,\n",
       "   'td_error': 153.7780558300682,\n",
       "   'init_value': -376.93389892578125,\n",
       "   'ave_value': -383.46135572522826}),\n",
       " (25,\n",
       "  {'time_sample_batch': 0.00032344050963536145,\n",
       "   'time_algorithm_update': 0.0909941667910261,\n",
       "   'temp_loss': 0.0010177633871230798,\n",
       "   'temp': 0.3617817800552272,\n",
       "   'alpha_loss': -0.015428371302688449,\n",
       "   'alpha': 2.249002670546602,\n",
       "   'critic_loss': 305.4925143580376,\n",
       "   'actor_loss': 382.35815847277917,\n",
       "   'time_step': 0.09166323983779405,\n",
       "   'td_error': 151.9830163939512,\n",
       "   'init_value': -372.828125,\n",
       "   'ave_value': -379.50794296732033}),\n",
       " (26,\n",
       "  {'time_sample_batch': 0.0003240840126680722,\n",
       "   'time_algorithm_update': 0.0907728812435758,\n",
       "   'temp_loss': 0.0012598659248829562,\n",
       "   'temp': 0.3618432446866883,\n",
       "   'alpha_loss': 0.05611779912422367,\n",
       "   'alpha': 2.232187041303027,\n",
       "   'critic_loss': 305.37339683809677,\n",
       "   'actor_loss': 381.0610024934553,\n",
       "   'time_step': 0.09144208100183455,\n",
       "   'td_error': 151.055012673217,\n",
       "   'init_value': -371.3172912597656,\n",
       "   'ave_value': -378.1840906400251}),\n",
       " (27,\n",
       "  {'time_sample_batch': 0.00032276747959040055,\n",
       "   'time_algorithm_update': 0.09020295370946581,\n",
       "   'temp_loss': -0.000996722697547054,\n",
       "   'temp': 0.3611607913779607,\n",
       "   'alpha_loss': 0.02553116674774932,\n",
       "   'alpha': 2.199727045780792,\n",
       "   'critic_loss': 303.8417127645181,\n",
       "   'actor_loss': 379.27491507409076,\n",
       "   'time_step': 0.0908678944887245,\n",
       "   'td_error': 151.1714805092278,\n",
       "   'init_value': -372.2703857421875,\n",
       "   'ave_value': -379.3775645807627}),\n",
       " (28,\n",
       "  {'time_sample_batch': 0.00032247547877578206,\n",
       "   'time_algorithm_update': 0.0905953502889043,\n",
       "   'temp_loss': -0.002661704557881138,\n",
       "   'temp': 0.3603977707083451,\n",
       "   'alpha_loss': 0.042370417688789214,\n",
       "   'alpha': 2.183184081907911,\n",
       "   'critic_loss': 303.5562005534458,\n",
       "   'actor_loss': 377.9231832649636,\n",
       "   'time_step': 0.09126210563727945,\n",
       "   'td_error': 150.64589724695094,\n",
       "   'init_value': -369.5162353515625,\n",
       "   'ave_value': -376.9608241176777}),\n",
       " (29,\n",
       "  {'time_sample_batch': 0.00032239536352576066,\n",
       "   'time_algorithm_update': 0.08971680284372378,\n",
       "   'temp_loss': 0.0030905455615364476,\n",
       "   'temp': 0.36107396472925124,\n",
       "   'alpha_loss': 0.049012036066280594,\n",
       "   'alpha': 2.150564372401865,\n",
       "   'critic_loss': 303.9037359236432,\n",
       "   'actor_loss': 377.110741952275,\n",
       "   'time_step': 0.09038400519252099,\n",
       "   'td_error': 152.11518715758464,\n",
       "   'init_value': -367.2611083984375,\n",
       "   'ave_value': -375.005281074438}),\n",
       " (30,\n",
       "  {'time_sample_batch': 0.0003254886794035209,\n",
       "   'time_algorithm_update': 0.09166120126946693,\n",
       "   'temp_loss': -0.0005623962813918425,\n",
       "   'temp': 0.3596801210934722,\n",
       "   'alpha_loss': -0.004097521271960137,\n",
       "   'alpha': 2.143575791433006,\n",
       "   'critic_loss': 303.5697760577152,\n",
       "   'actor_loss': 375.7562272113685,\n",
       "   'time_step': 0.09233160406419917,\n",
       "   'td_error': 151.80717670504805,\n",
       "   'init_value': -366.3521728515625,\n",
       "   'ave_value': -374.31117526765775}),\n",
       " (31,\n",
       "  {'time_sample_batch': 0.0003232552259128033,\n",
       "   'time_algorithm_update': 0.0894827664793226,\n",
       "   'temp_loss': 0.00012451626421447078,\n",
       "   'temp': 0.36164807951116534,\n",
       "   'alpha_loss': 0.053641710409832885,\n",
       "   'alpha': 2.126187172518316,\n",
       "   'critic_loss': 301.85631993123894,\n",
       "   'actor_loss': 373.8286955307042,\n",
       "   'time_step': 0.09015002082732203,\n",
       "   'td_error': 149.9945924684128,\n",
       "   'init_value': -364.87469482421875,\n",
       "   'ave_value': -373.04969961284945}),\n",
       " (32,\n",
       "  {'time_sample_batch': 0.0003223392002577044,\n",
       "   'time_algorithm_update': 0.09044677290988024,\n",
       "   'temp_loss': 0.0016147525276056884,\n",
       "   'temp': 0.3578099250991336,\n",
       "   'alpha_loss': 0.03625635007326538,\n",
       "   'alpha': 2.090304651037375,\n",
       "   'critic_loss': 300.0765135741812,\n",
       "   'actor_loss': 371.7944967212633,\n",
       "   'time_step': 0.09111578439041869,\n",
       "   'td_error': 148.13623125843387,\n",
       "   'init_value': -361.79571533203125,\n",
       "   'ave_value': -370.2364439860851}),\n",
       " (33,\n",
       "  {'time_sample_batch': 0.0003229059605895785,\n",
       "   'time_algorithm_update': 0.09017169568472591,\n",
       "   'temp_loss': 0.00011560220318264597,\n",
       "   'temp': 0.3566744191653803,\n",
       "   'alpha_loss': 0.010538321456203615,\n",
       "   'alpha': 2.0762205004829735,\n",
       "   'critic_loss': 296.5471138163334,\n",
       "   'actor_loss': 368.3070477756562,\n",
       "   'time_step': 0.09084160075314326,\n",
       "   'td_error': 146.4533405497028,\n",
       "   'init_value': -356.5564880371094,\n",
       "   'ave_value': -365.1387829347559}),\n",
       " (34,\n",
       "  {'time_sample_batch': 0.0003215058020576318,\n",
       "   'time_algorithm_update': 0.09044981925096578,\n",
       "   'temp_loss': -0.0010950191072285106,\n",
       "   'temp': 0.35856941925667174,\n",
       "   'alpha_loss': 0.04142675211807505,\n",
       "   'alpha': 2.071470718013497,\n",
       "   'critic_loss': 291.8196984599699,\n",
       "   'actor_loss': 364.3278335016264,\n",
       "   'time_step': 0.09111290650472355,\n",
       "   'td_error': 145.6282216482493,\n",
       "   'init_value': -352.9852600097656,\n",
       "   'ave_value': -361.8486650639182}),\n",
       " (35,\n",
       "  {'time_sample_batch': 0.00032443444805938155,\n",
       "   'time_algorithm_update': 0.09131960811708596,\n",
       "   'temp_loss': -0.0015185632508430376,\n",
       "   'temp': 0.3589464257671293,\n",
       "   'alpha_loss': 0.014045644557186793,\n",
       "   'alpha': 2.0533931623567887,\n",
       "   'critic_loss': 286.35663724558185,\n",
       "   'actor_loss': 360.7822869897715,\n",
       "   'time_step': 0.09199312780112648,\n",
       "   'td_error': 141.00195396904383,\n",
       "   'init_value': -348.13836669921875,\n",
       "   'ave_value': -356.93436600269285}),\n",
       " (36,\n",
       "  {'time_sample_batch': 0.0003249190971427371,\n",
       "   'time_algorithm_update': 0.0910941548559462,\n",
       "   'temp_loss': 0.00042742281694714774,\n",
       "   'temp': 0.35977009587058706,\n",
       "   'alpha_loss': -0.013534218509742756,\n",
       "   'alpha': 2.0500835082547493,\n",
       "   'critic_loss': 281.40552348146264,\n",
       "   'actor_loss': 356.75915294761745,\n",
       "   'time_step': 0.0917667726928037,\n",
       "   'td_error': 138.4451647975536,\n",
       "   'init_value': -343.50421142578125,\n",
       "   'ave_value': -352.4618402976861}),\n",
       " (37,\n",
       "  {'time_sample_batch': 0.00032253676969514985,\n",
       "   'time_algorithm_update': 0.08986017923569845,\n",
       "   'temp_loss': -0.0010712851957951975,\n",
       "   'temp': 0.360286415616958,\n",
       "   'alpha_loss': -0.01475848322492601,\n",
       "   'alpha': 2.0511225345641306,\n",
       "   'critic_loss': 278.27052969139663,\n",
       "   'actor_loss': 354.3621027640327,\n",
       "   'time_step': 0.0905305280008162,\n",
       "   'td_error': 137.7702907378289,\n",
       "   'init_value': -343.20147705078125,\n",
       "   'ave_value': -352.1003512276787}),\n",
       " (38,\n",
       "  {'time_sample_batch': 0.0003248480671272542,\n",
       "   'time_algorithm_update': 0.09184583395237736,\n",
       "   'temp_loss': 0.00012206523104177736,\n",
       "   'temp': 0.36190666509985786,\n",
       "   'alpha_loss': 0.0034238402221727455,\n",
       "   'alpha': 2.044888884723875,\n",
       "   'critic_loss': 275.5247867621533,\n",
       "   'actor_loss': 352.3196665343181,\n",
       "   'time_step': 0.0925158032024705,\n",
       "   'td_error': 137.03004360076602,\n",
       "   'init_value': -341.3427429199219,\n",
       "   'ave_value': -350.2393808206395}),\n",
       " (39,\n",
       "  {'time_sample_batch': 0.0003210365359282108,\n",
       "   'time_algorithm_update': 0.09014628548820354,\n",
       "   'temp_loss': 0.0022400938514923873,\n",
       "   'temp': 0.36381051433433975,\n",
       "   'alpha_loss': 0.04880675139081679,\n",
       "   'alpha': 2.0301015755596117,\n",
       "   'critic_loss': 274.2025833403128,\n",
       "   'actor_loss': 350.8632940199854,\n",
       "   'time_step': 0.09081239499340982,\n",
       "   'td_error': 137.92932967987878,\n",
       "   'init_value': -341.0992126464844,\n",
       "   'ave_value': -349.7714659517542}),\n",
       " (40,\n",
       "  {'time_sample_batch': 0.0003227231546582581,\n",
       "   'time_algorithm_update': 0.09107090109490357,\n",
       "   'temp_loss': -0.0017881930465167431,\n",
       "   'temp': 0.3647903068675111,\n",
       "   'alpha_loss': 0.063628239421584,\n",
       "   'alpha': 2.020732377799912,\n",
       "   'critic_loss': 273.50766204769013,\n",
       "   'actor_loss': 350.067744420252,\n",
       "   'time_step': 0.09173897290752886,\n",
       "   'td_error': 134.35094199225546,\n",
       "   'init_value': -337.4706115722656,\n",
       "   'ave_value': -346.0951132238864})]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_episodes,\n",
    "        eval_episodes=test_episodes,\n",
    "        n_epochs=40, \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": "code",
   "execution_count": 10,
   "id": "69ff770c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/d3rlpy/preprocessing/action_scalers.py:204: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
      "  minimum = torch.tensor(\n",
      "/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/d3rlpy/preprocessing/action_scalers.py:207: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
      "  maximum = torch.tensor(\n"
     ]
    }
   ],
   "source": [
    "model.save_model('cqlStochC2000_Ep40model_CPUOnly.pt')\n",
    "model.save_policy('cqlStochC2000_Ep40_CPUOnly.pt')"
   ]
  },
  {
   "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": [],
   "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+2000 for i in range(100)]) #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": [],
   "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": 13,
   "id": "dd27fe4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from d3rlpy.torch_utility import to_cpu\n",
    "# to_cpu(model)\n",
    "# model.save_policy(\"cqlStochpid2000Ep40CPU.pt\")\n",
    "# model.save_model(\"cqlStochpid2000Ep40modelCPU.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7ccd9eb3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for key in dir(model):\n",
    "#     module = getattr(model, key)\n",
    "#     if isinstance(module, (torch.nn.Module, torch.nn.Parameter)):\n",
    "#         print(yes)\n",
    "#         print(key)\n",
    "# dir(model)\n",
    "# type(model)\n",
    "# model.cpu()\n",
    "# from d3rlpy.algos.torch.base import TorchImplBase\n",
    "# new_model = TorchImplBase()\n",
    "# from d3rlpy.torch_utility import _get_attributes\n",
    "# model._device = \"cpu:0\"\n",
    "# print(model._device)\n",
    "\n",
    "\n",
    "# def my_get_state_dict(impl: Any) -> Dict[str, Any]:\n",
    "#     rets = {}\n",
    "#     for key in _get_attributes(impl):\n",
    "#         obj = getattr(impl, key)\n",
    "#         if isinstance(obj, (torch.nn.Module, torch.optim.Optimizer)):\n",
    "#             if isinstance(obj, (torch.nn.Module, torch.nn.Parameter)):\n",
    "#                 obj.cpu()\n",
    "#             rets[key] = obj.state_dict()\n",
    "#     return rets\n",
    "\n",
    "# torch.save(my_get_state_dict(model), \"my_test_model.pt\")\n",
    "\n",
    "# for key in dir(model):\n",
    "#     obj = getattr(model, key)\n",
    "#     if isinstance(obj, (torch.nn.Module, torch.nn.Parameter)):\n",
    "#         obj.cpu()\n",
    "#         print(\"convert to cpu\")\n",
    "# model.save_policy(\"cqlStochpid2000Ep40modelCPU.pt\")\n",
    "\n",
    "# import trace\n",
    "# tracer = trace.Trace()\n",
    "# tracer.run('model.save_policy(\"cqlStochpid2000Ep40modelCPU.pt\")')"
   ]
  }
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