workflowDet998.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_det_small.txt\") -> d3rlpy.dataset.MDPDataset :\n",
    "    random.seed(0)\n",
    "    samples = DataSampler(path_to_data=path)\n",
    "    states = []\n",
    "    actions = []\n",
    "    rewards = []\n",
    "    next_states = []\n",
    "    for chunk in chunks:\n",
    "        samples.use_chunk(chunk)\n",
    "        samples.read_chunk()\n",
    "        [statesChunk, actionsChunk, rewardsChunk, nextStatesChunk] = samples.get_batch(batch_size)\n",
    "        states.append(statesChunk)\n",
    "        actions.append(actionsChunk)\n",
    "        rewards.append(rewardsChunk)\n",
    "        next_states.append(nextStatesChunk)\n",
    "    states = torch.cat(states)\n",
    "    actions = torch.cat(actions)\n",
    "    rewards = torch.cat(rewards)\n",
    "    next_states = torch.cat(next_states)\n",
    "    terminals = np.zeros(len(states))\n",
    "    terminals[::100] = 1 #episode length 100, change if necessary\n",
    "    print(states.shape)\n",
    "    dataset = d3rlpy.dataset.MDPDataset(states.numpy(), \n",
    "                                        actions.numpy(), \n",
    "                                        rewards.numpy(), terminals)\n",
    "    return dataset, states.numpy(), actions.numpy(), rewards.numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eeaff27f-fd9a-4ad2-81c6-fca5f5fe6ce9",
   "metadata": {},
   "source": [
    "We can build the dataset from there, just like this, and split into train and test sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f543168b-2603-47ed-bff8-d0bafb40bb0f",
   "metadata": {},
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.41789108e-01 -1.99995308e-04\n",
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    },
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     "text": [
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    },
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -1.15389108e-01  2.20000469e-03\n",
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     "text": [
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    },
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    },
    {
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08  4.45108923e-02  1.22000047e-02\n",
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    },
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    },
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08  6.11089229e-03 -5.99995308e-04\n",
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    },
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    },
    {
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     "output_type": "stream",
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     "text": [
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    },
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     "text": [
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    },
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     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -4.30891077e-02  4.69199998e-09\n",
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     ]
    },
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08  1.09010892e-01  1.94000047e-02\n",
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    },
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    },
    {
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     "text": [
      "[ 0.00000000e+00  7.95731469e+08 -8.62891077e-02 -1.99999953e-02\n",
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      "start\n"
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    },
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     "name": "stdout",
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      "[ 0.00000000e+00  7.95731469e+08 -2.28910771e-03  1.08000047e-02\n",
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      "[ 0.00000000e+00  7.95731469e+08 -4.36891077e-02  3.80000469e-03\n",
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      " -1.39717313e-01 -8.61271423e-02  4.13835437e-01]\n",
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      "torch.Size([27714460, 6])\n"
     ]
    }
   ],
   "source": [
    "dataset, states, actions, rewards = get_dataset([i for i in range(998)], path=\"collected_data/rl_deterministic.txt\")"
   ]
  },
  {
   "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': -4.2979746,\n",
       " 'std': 2.9650762,\n",
       " 'min': -19.356209,\n",
       " 'max': 0.0,\n",
       " 'histogram': (array([   21,    43,    95,   379,  1195,  1979,  2612,  3300,  4137,\n",
       "          5446,  7025,  8735, 11719, 18487, 25193, 33395, 40758, 54770,\n",
       "         46735, 11121]),\n",
       "  array([-19.356209  , -18.3884    , -17.420588  , -16.452778  ,\n",
       "         -15.484967  , -14.517157  , -13.549346  , -12.581535  ,\n",
       "         -11.613726  , -10.645915  ,  -9.678104  ,  -8.710294  ,\n",
       "          -7.7424836 ,  -6.774673  ,  -5.806863  ,  -4.839052  ,\n",
       "          -3.8712418 ,  -2.9034314 ,  -1.9356209 ,  -0.96781045,\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": "f755a889-59df-434a-9f60-e9e9bee7187b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(states[:,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "70e304fb-35e2-44ec-b703-201d7bdaa0dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9f0259c2-f04b-41ad-af93-211c96e7d824",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot(model.predict(np.array(states)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "id": "f5ef782b-6847-4fa4-8d95-3fa8904866bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from d3rlpy.algos import CQL\n",
    "from d3rlpy.models.encoders import VectorEncoderFactory\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",
    "actor_encoder = VectorEncoderFactory(hidden_units=[12, 24, 36, 24, 12],\n",
    "                                       activation='relu', use_batch_norm=True, dropout_rate=0.2)\n",
    "critic_encoder = VectorEncoderFactory(hidden_units=[12, 24, 24, 12],\n",
    "                                       activation='relu', use_batch_norm=True, dropout_rate=0.2)\n",
    "\n",
    "model = CQL(q_func_factory='qr', #qr -> quantile regression q function, but you don't have to use this\n",
    "            reward_scaler='standard',\n",
    "            actor_encoder_factory = actor_encoder,\n",
    "            critic_encoder_factory = critic_encoder,\n",
    "            action_scaler=action_scaler,\n",
    "          actor_learning_rate=1e-5, \n",
    "          critic_learning_rate=0.0003, \n",
    "            use_gpu=True) #change it to true if you have one\n",
    "model.build_with_dataset(dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2fd44e0e-0ab2-4ab0-8f96-7a8eb382cda0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.019474013344166884\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": 11,
   "id": "8f69d169-0f59-4e77-8654-c5d79dae2b1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#%load_ext tensorboard\n",
    "#%tensorboard --logdir runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b0384d5-a843-4d30-a355-404215f27fa4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 19:48.22 [debug    ] RoundIterator is selected.\n",
      "2022-04-15 19:48.22 [info     ] Directory is created at d3rlpy_logs/CQL_20220415194822\n",
      "2022-04-15 19:48.22 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-15 19:48.22 [debug    ] Fitting reward scaler...       reward_scaler=standard\n",
      "2022-04-15 19:48.23 [warning  ] Skip building models since they're already built.\n",
      "2022-04-15 19:48.23 [info     ] Parameters are saved to d3rlpy_logs/CQL_20220415194822/params.json params={'action_scaler': {'type': 'min_max', 'params': {'minimum': array(-0.6), 'maximum': array(0.6)}}, 'actor_encoder_factory': {'type': 'vector', 'params': {'hidden_units': [12, 24, 36, 24, 12], 'activation': 'relu', 'use_batch_norm': True, 'dropout_rate': 0.2, 'use_dense': False}}, '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': 'vector', 'params': {'hidden_units': [12, 24, 24, 12], 'activation': 'relu', 'use_batch_norm': True, 'dropout_rate': 0.2, 'use_dense': False}}, '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': 'qr', 'params': {'bootstrap': False, 'share_encoder': False, 'n_quantiles': 32}}, 'real_ratio': 1.0, 'reward_scaler': {'type': 'standard', 'params': {'mean': -0.043404124311562896, 'std': 0.0299782673584534, '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': 0, 'algorithm': 'CQL', 'observation_shape': (6,), 'action_size': 3}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "75659dd52a714a9b8cd46233a3b3cd1e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 20:27.40 [info     ] CQL_20220415194822: epoch=1 step=85741 epoch=1 metrics={'time_sample_batch': 0.00031275933899537264, 'time_algorithm_update': 0.020723903086432908, 'temp_loss': 0.5277301647774326, 'temp': 0.1243415923763854, 'alpha_loss': -19313.581439918868, 'alpha': 1318.5249205411626, 'critic_loss': 17695.369889204558, 'actor_loss': 2.185401217090781, 'time_step': 0.02453802335468208, 'td_error': 1.3116502787876172, 'init_value': -5.9981889724731445, 'ave_value': -5.998183777200564} step=85741\n",
      "2022-04-15 20:27.40 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_85741.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9ff6838333b44e579e59ec31ed2db2b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 2/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 21:07.40 [info     ] CQL_20220415194822: epoch=2 step=171482 epoch=2 metrics={'time_sample_batch': 0.0003043265410616692, 'time_algorithm_update': 0.02055568481845553, 'temp_loss': 0.00015883790537005648, 'temp': 0.0008094566549346069, 'alpha_loss': -8375901.8132385975, 'alpha': 640866.8451333335, 'critic_loss': 7871029.914287469, 'actor_loss': 10.528318059189855, 'time_step': 0.025112815303733306, 'td_error': 2.360244169918384, 'init_value': -11.755678176879883, 'ave_value': -11.755678390894605} step=171482\n",
      "2022-04-15 21:07.40 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_171482.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d2710401dea4b32922db11b39989019",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 3/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 21:47.49 [info     ] CQL_20220415194822: epoch=3 step=257223 epoch=3 metrics={'time_sample_batch': 0.00030402676114102704, 'time_algorithm_update': 0.020606310450087106, 'temp_loss': 0.00015607679701247522, 'temp': 0.00033537459650869155, 'alpha_loss': -13383317.610349774, 'alpha': 1001080.3125, 'critic_loss': 12327216.323042652, 'actor_loss': 11.624818662523152, 'time_step': 0.025162534040486042, 'td_error': 2.2134307715695707, 'init_value': -11.188474655151367, 'ave_value': -11.188474011703462} step=257223\n",
      "2022-04-15 21:47.49 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_257223.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "21d67c9b245642a8b0ecfecc7a27ceee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 4/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 22:28.13 [info     ] CQL_20220415194822: epoch=4 step=342964 epoch=4 metrics={'time_sample_batch': 0.0003065771539428083, 'time_algorithm_update': 0.020708899227647796, 'temp_loss': -2.1391722713341628e-05, 'temp': 0.00015364132093558856, 'alpha_loss': -12327037.557189675, 'alpha': 1001080.3125, 'critic_loss': 12140400.615971355, 'actor_loss': 12.278944450913466, 'time_step': 0.02535528280262828, 'td_error': 2.846814049539306, 'init_value': -13.689773559570312, 'ave_value': -13.68977322760292} step=342964\n",
      "2022-04-15 22:28.13 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_342964.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df26d32dc4014ce4b098801f01b9e21f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 5/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 23:08.26 [info     ] CQL_20220415194822: epoch=5 step=428705 epoch=5 metrics={'time_sample_batch': 0.00030470813146989313, 'time_algorithm_update': 0.020629897705840523, 'temp_loss': -4.2312726479041704e-06, 'temp': 0.00014953832779034188, 'alpha_loss': -12505671.227988943, 'alpha': 1001080.3125, 'critic_loss': 12171208.153940355, 'actor_loss': 14.992480897295586, 'time_step': 0.02524293660964616, 'td_error': 3.144021573664416, 'init_value': -14.715094566345215, 'ave_value': -14.71509383888025} step=428705\n",
      "2022-04-15 23:08.26 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_428705.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5b200c4761e247f2a80149335aa129ae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 6/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-15 23:48.40 [info     ] CQL_20220415194822: epoch=6 step=514446 epoch=6 metrics={'time_sample_batch': 0.000304875406263101, 'time_algorithm_update': 0.020652849072123468, 'temp_loss': 1.08136075050775e-06, 'temp': 5.8964901937962406e-05, 'alpha_loss': -12284862.400275247, 'alpha': 1001080.3125, 'critic_loss': 12085494.017751135, 'actor_loss': 13.579759732074209, 'time_step': 0.025258611466810896, 'td_error': 2.615160139787588, 'init_value': -12.83185863494873, 'ave_value': -12.831860048715814} step=514446\n",
      "2022-04-15 23:48.40 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_514446.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "02c51456761f454c802c22ce4989d375",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 7/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 00:29.07 [info     ] CQL_20220415194822: epoch=7 step=600187 epoch=7 metrics={'time_sample_batch': 0.00030416193850525024, 'time_algorithm_update': 0.020700133276030763, 'temp_loss': 2.8629059917853134e-06, 'temp': 7.974594527920217e-05, 'alpha_loss': -12370957.113609591, 'alpha': 1001080.3125, 'critic_loss': 12100587.54939877, 'actor_loss': 11.931004624135351, 'time_step': 0.025369210033617178, 'td_error': 2.2850078968282594, 'init_value': -11.500444412231445, 'ave_value': -11.500446219969144} step=600187\n",
      "2022-04-16 00:29.07 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_600187.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5ac628fbe75749b0b8ca85932076629a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 8/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 01:09.32 [info     ] CQL_20220415194822: epoch=8 step=685928 epoch=8 metrics={'time_sample_batch': 0.0003054375130790892, 'time_algorithm_update': 0.02066124657206154, 'temp_loss': 4.2987725179149425e-06, 'temp': 4.682954016648013e-05, 'alpha_loss': -12387734.390676573, 'alpha': 1001080.3125, 'critic_loss': 12113971.474160554, 'actor_loss': 10.789485654162345, 'time_step': 0.02537421233297455, 'td_error': 2.2064208532309633, 'init_value': -11.162444114685059, 'ave_value': -11.162443161010742} step=685928\n",
      "2022-04-16 01:09.32 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_685928.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3e0fb24643e44b9f900b900c6f4693e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 9/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 01:50.03 [info     ] CQL_20220415194822: epoch=9 step=771669 epoch=9 metrics={'time_sample_batch': 0.00030707452922892274, 'time_algorithm_update': 0.020735845929954203, 'temp_loss': -1.128560036883189e-06, 'temp': 3.86948897100545e-05, 'alpha_loss': -12239177.316546343, 'alpha': 1001080.3125, 'critic_loss': 12065161.480050385, 'actor_loss': 10.028992998721105, 'time_step': 0.025429588701093762, 'td_error': 1.7640987986467795, 'init_value': -9.013802528381348, 'ave_value': -9.013804238063573} step=771669\n",
      "2022-04-16 01:50.03 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_771669.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "014a031afcd547bbaf4b19e8014f8ddc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 10/10:   0%|          | 0/85741 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.34 [info     ] CQL_20220415194822: epoch=10 step=857410 epoch=10 metrics={'time_sample_batch': 0.0003076755517096982, 'time_algorithm_update': 0.020737744080086183, 'temp_loss': 4.55514840591495e-08, 'temp': 5.123224858212597e-05, 'alpha_loss': -12257118.904223183, 'alpha': 1001080.3125, 'critic_loss': 12076661.861279901, 'actor_loss': 7.639729850049536, 'time_step': 0.025443260273266214, 'td_error': 1.3771597688444286, 'init_value': -6.577911853790283, 'ave_value': -6.577912109429451} step=857410\n",
      "2022-04-16 02:30.34 [info     ] Model parameters are saved to d3rlpy_logs/CQL_20220415194822/model_857410.pt\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(1,\n",
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       "   'temp': 0.1243415923763854,\n",
       "   'alpha_loss': -19313.581439918868,\n",
       "   'alpha': 1318.5249205411626,\n",
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       "   'init_value': -5.9981889724731445,\n",
       "   'ave_value': -5.998183777200564}),\n",
       " (2,\n",
       "  {'time_sample_batch': 0.0003043265410616692,\n",
       "   'time_algorithm_update': 0.02055568481845553,\n",
       "   'temp_loss': 0.00015883790537005648,\n",
       "   'temp': 0.0008094566549346069,\n",
       "   'alpha_loss': -8375901.8132385975,\n",
       "   'alpha': 640866.8451333335,\n",
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       "   'actor_loss': 10.528318059189855,\n",
       "   'time_step': 0.025112815303733306,\n",
       "   'td_error': 2.360244169918384,\n",
       "   'init_value': -11.755678176879883,\n",
       "   'ave_value': -11.755678390894605}),\n",
       " (3,\n",
       "  {'time_sample_batch': 0.00030402676114102704,\n",
       "   'time_algorithm_update': 0.020606310450087106,\n",
       "   'temp_loss': 0.00015607679701247522,\n",
       "   'temp': 0.00033537459650869155,\n",
       "   'alpha_loss': -13383317.610349774,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12327216.323042652,\n",
       "   'actor_loss': 11.624818662523152,\n",
       "   'time_step': 0.025162534040486042,\n",
       "   'td_error': 2.2134307715695707,\n",
       "   'init_value': -11.188474655151367,\n",
       "   'ave_value': -11.188474011703462}),\n",
       " (4,\n",
       "  {'time_sample_batch': 0.0003065771539428083,\n",
       "   'time_algorithm_update': 0.020708899227647796,\n",
       "   'temp_loss': -2.1391722713341628e-05,\n",
       "   'temp': 0.00015364132093558856,\n",
       "   'alpha_loss': -12327037.557189675,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12140400.615971355,\n",
       "   'actor_loss': 12.278944450913466,\n",
       "   'time_step': 0.02535528280262828,\n",
       "   'td_error': 2.846814049539306,\n",
       "   'init_value': -13.689773559570312,\n",
       "   'ave_value': -13.68977322760292}),\n",
       " (5,\n",
       "  {'time_sample_batch': 0.00030470813146989313,\n",
       "   'time_algorithm_update': 0.020629897705840523,\n",
       "   'temp_loss': -4.2312726479041704e-06,\n",
       "   'temp': 0.00014953832779034188,\n",
       "   'alpha_loss': -12505671.227988943,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12171208.153940355,\n",
       "   'actor_loss': 14.992480897295586,\n",
       "   'time_step': 0.02524293660964616,\n",
       "   'td_error': 3.144021573664416,\n",
       "   'init_value': -14.715094566345215,\n",
       "   'ave_value': -14.71509383888025}),\n",
       " (6,\n",
       "  {'time_sample_batch': 0.000304875406263101,\n",
       "   'time_algorithm_update': 0.020652849072123468,\n",
       "   'temp_loss': 1.08136075050775e-06,\n",
       "   'temp': 5.8964901937962406e-05,\n",
       "   'alpha_loss': -12284862.400275247,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12085494.017751135,\n",
       "   'actor_loss': 13.579759732074209,\n",
       "   'time_step': 0.025258611466810896,\n",
       "   'td_error': 2.615160139787588,\n",
       "   'init_value': -12.83185863494873,\n",
       "   'ave_value': -12.831860048715814}),\n",
       " (7,\n",
       "  {'time_sample_batch': 0.00030416193850525024,\n",
       "   'time_algorithm_update': 0.020700133276030763,\n",
       "   'temp_loss': 2.8629059917853134e-06,\n",
       "   'temp': 7.974594527920217e-05,\n",
       "   'alpha_loss': -12370957.113609591,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12100587.54939877,\n",
       "   'actor_loss': 11.931004624135351,\n",
       "   'time_step': 0.025369210033617178,\n",
       "   'td_error': 2.2850078968282594,\n",
       "   'init_value': -11.500444412231445,\n",
       "   'ave_value': -11.500446219969144}),\n",
       " (8,\n",
       "  {'time_sample_batch': 0.0003054375130790892,\n",
       "   'time_algorithm_update': 0.02066124657206154,\n",
       "   'temp_loss': 4.2987725179149425e-06,\n",
       "   'temp': 4.682954016648013e-05,\n",
       "   'alpha_loss': -12387734.390676573,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12113971.474160554,\n",
       "   'actor_loss': 10.789485654162345,\n",
       "   'time_step': 0.02537421233297455,\n",
       "   'td_error': 2.2064208532309633,\n",
       "   'init_value': -11.162444114685059,\n",
       "   'ave_value': -11.162443161010742}),\n",
       " (9,\n",
       "  {'time_sample_batch': 0.00030707452922892274,\n",
       "   'time_algorithm_update': 0.020735845929954203,\n",
       "   'temp_loss': -1.128560036883189e-06,\n",
       "   'temp': 3.86948897100545e-05,\n",
       "   'alpha_loss': -12239177.316546343,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12065161.480050385,\n",
       "   'actor_loss': 10.028992998721105,\n",
       "   'time_step': 0.025429588701093762,\n",
       "   'td_error': 1.7640987986467795,\n",
       "   'init_value': -9.013802528381348,\n",
       "   'ave_value': -9.013804238063573}),\n",
       " (10,\n",
       "  {'time_sample_batch': 0.0003076755517096982,\n",
       "   'time_algorithm_update': 0.020737744080086183,\n",
       "   'temp_loss': 4.55514840591495e-08,\n",
       "   'temp': 5.123224858212597e-05,\n",
       "   'alpha_loss': -12257118.904223183,\n",
       "   'alpha': 1001080.3125,\n",
       "   'critic_loss': 12076661.861279901,\n",
       "   'actor_loss': 7.639729850049536,\n",
       "   'time_step': 0.025443260273266214,\n",
       "   'td_error': 1.3771597688444286,\n",
       "   'init_value': -6.577911853790283,\n",
       "   'ave_value': -6.577912109429451})]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_episodes,\n",
    "        eval_episodes=test_episodes,\n",
    "        n_epochs=10, \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": 15,
   "id": "51888e50",
   "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_policy(\"cqlDet998.pt\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "6c580e37",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model(\"cqlDet998model.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": 13,
   "id": "de79cadb-28ef-42f8-b230-756f3ee11bb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.03891077e-02 -1.41999953e-02\n",
      " -2.10001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -1.78778459e-03 -1.34615461e-02  4.84073546e-02]\n",
      "Read chunk # 3 out of 10000\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -7.24891077e-02 -1.35999953e-02\n",
      " -4.20001342e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      " -6.23311010e-02 -1.64283998e-01  6.00000000e-01]\n",
      "Read chunk # 5 out of 10000\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08  7.01089229e-03 -4.19999531e-03\n",
      "  7.39998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.21623335e-01 -2.86362315e-02 -8.00043364e-02]\n",
      "Read chunk # 7 out of 10000\n",
      "start\n",
      "[ 0.00000000e+00  7.95731469e+08 -1.03989108e-01 -1.37999953e-02\n",
      "  7.99998658e-03  0.00000000e+00 -5.33423489e+00 -1.57091618e+00\n",
      "  2.76352555e-01 -3.26280816e-01  6.00000000e-01]\n",
      "Read chunk # 9 out of 10000\n",
      "torch.Size([111080, 6])\n",
      "2022-04-16 02:30.35 [debug    ] RoundIterator is selected.\n",
      "2022-04-16 02:30.35 [info     ] Directory is created at d3rlpy_logs/FQE_20220416023035\n",
      "2022-04-16 02:30.35 [debug    ] Fitting action scaler...       action_scaler=min_max\n",
      "2022-04-16 02:30.35 [debug    ] Building models...\n",
      "2022-04-16 02:30.35 [debug    ] Models have been built.\n",
      "2022-04-16 02:30.35 [info     ] Parameters are saved to d3rlpy_logs/FQE_20220416023035/params.json params={'action_scaler': {'type': 'min_max', 'params': {'minimum': array(-0.6), 'maximum': array(0.6)}}, 'batch_size': 100, 'encoder_factory': {'type': 'default', 'params': {'activation': 'relu', 'use_batch_norm': False, 'dropout_rate': None}}, 'gamma': 0.99, 'generated_maxlen': 100000, 'learning_rate': 0.0001, 'n_critics': 1, 'n_frames': 1, 'n_steps': 1, 'optim_factory': {'optim_cls': 'Adam', 'betas': (0.9, 0.999), 'eps': 1e-08, 'weight_decay': 0, 'amsgrad': False}, 'q_func_factory': {'type': 'mean', 'params': {'bootstrap': False, 'share_encoder': False}}, 'real_ratio': 1.0, 'reward_scaler': None, 'scaler': None, 'target_update_interval': 100, 'use_gpu': None, 'algorithm': 'FQE', 'observation_shape': (6,), 'action_size': 3}\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bef2bb8330ad4e99a9f879b42e2e0ca5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 1/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/numpy/core/fromnumeric.py:3440: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/dasc/anaconda3/envs/jbreeden3.10/lib/python3.10/site-packages/numpy/core/_methods.py:189: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n",
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.38 [info     ] FQE_20220416023035: epoch=1 step=878 epoch=1 metrics={'time_sample_batch': 0.00011914167425898593, 'time_algorithm_update': 0.0029855746614634313, 'loss': 0.0002461599849627247, 'time_step': 0.003155057142427136, 'init_value': -0.22843682765960693, 'ave_value': -0.22831709708927517, 'soft_opc': nan} step=878\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.38 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_878.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a1822e656b374fb0b2e935ad0137fe71",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.42 [info     ] FQE_20220416023035: epoch=2 step=1756 epoch=2 metrics={'time_sample_batch': 0.0001231980486719918, 'time_algorithm_update': 0.0030236969233102297, 'loss': 0.0014282974988773878, 'time_step': 0.0031988797263838437, 'init_value': -0.5504694581031799, 'ave_value': -0.5503293672049991, 'soft_opc': nan} step=1756\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.42 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_1756.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d42e55bcb50440a78c464e94970f5069",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 3/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.46 [info     ] FQE_20220416023035: epoch=3 step=2634 epoch=3 metrics={'time_sample_batch': 0.000123508698847951, 'time_algorithm_update': 0.0031934004438222132, 'loss': 0.004029400426500787, 'time_step': 0.003368657922418894, 'init_value': -0.787994921207428, 'ave_value': -0.7878976750239928, 'soft_opc': nan} step=2634\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.46 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_2634.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f34ac9d85b6648b69da8a33ebe9d1763",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.49 [info     ] FQE_20220416023035: epoch=4 step=3512 epoch=4 metrics={'time_sample_batch': 0.000124397473346128, 'time_algorithm_update': 0.0031993448870057126, 'loss': 0.007525220854168146, 'time_step': 0.003378618822553978, 'init_value': -1.0022207498550415, 'ave_value': -1.002360916034561, 'soft_opc': nan} step=3512\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.49 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_3512.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0a089155f63746d9bc0de06551745abb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 5/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.54 [info     ] FQE_20220416023035: epoch=5 step=4390 epoch=5 metrics={'time_sample_batch': 0.00013417480746814493, 'time_algorithm_update': 0.0035719817211524773, 'loss': 0.010765274227209073, 'time_step': 0.0037644083244653933, 'init_value': -1.1627755165100098, 'ave_value': -1.1630126333066064, 'soft_opc': nan} step=4390\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.54 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_4390.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d46fb89af8944206bc87b1cebaabd235",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.57 [info     ] FQE_20220416023035: epoch=6 step=5268 epoch=6 metrics={'time_sample_batch': 0.00012600747762345508, 'time_algorithm_update': 0.003410469426652565, 'loss': 0.013091343081994302, 'time_step': 0.003584749877860171, 'init_value': -1.2380139827728271, 'ave_value': -1.2383038729446794, 'soft_opc': nan} step=5268\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:30.57 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_5268.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fec5c14003334252a7a97367d7f9e9b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.02 [info     ] FQE_20220416023035: epoch=7 step=6146 epoch=7 metrics={'time_sample_batch': 0.00012883319941631483, 'time_algorithm_update': 0.003542760781655279, 'loss': 0.016386731998706525, 'time_step': 0.0037237403061503973, 'init_value': -1.3720546960830688, 'ave_value': -1.3723281069705142, 'soft_opc': nan} step=6146\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.02 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_6146.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "17d4fc5c9e94403d86aa026b1055e35a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 8/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
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     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.06 [info     ] FQE_20220416023035: epoch=8 step=7024 epoch=8 metrics={'time_sample_batch': 0.00013460195146008884, 'time_algorithm_update': 0.0037369548867123544, 'loss': 0.018053457120000026, 'time_step': 0.003923984214765336, 'init_value': -1.415456771850586, 'ave_value': -1.4159563347314554, 'soft_opc': nan} step=7024\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.06 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_7024.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b6ef8aefc5f442babc70315c396cc6a1",
       "version_major": 2,
       "version_minor": 0
      },
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.10 [info     ] FQE_20220416023035: epoch=9 step=7902 epoch=9 metrics={'time_sample_batch': 0.00012868873622259953, 'time_algorithm_update': 0.0035864019719777725, 'loss': 0.020974107120380222, 'time_step': 0.0037703375609969224, 'init_value': -1.553494930267334, 'ave_value': -1.5538333263033062, 'soft_opc': nan} step=7902\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.10 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_7902.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a6ef2367c0f94dd2b44cde4e81efc9f4",
       "version_major": 2,
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      },
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.14 [info     ] FQE_20220416023035: epoch=10 step=8780 epoch=10 metrics={'time_sample_batch': 0.00012621113814091086, 'time_algorithm_update': 0.0035442499473589155, 'loss': 0.023965579658403274, 'time_step': 0.0037206905577883364, 'init_value': -1.6163628101348877, 'ave_value': -1.616753696293785, 'soft_opc': nan} step=8780\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.14 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_8780.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee7270b33fbf4939b2cd2c43de6d3369",
       "version_major": 2,
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     },
     "metadata": {},
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    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.18 [info     ] FQE_20220416023035: epoch=11 step=9658 epoch=11 metrics={'time_sample_batch': 0.00013945586045945152, 'time_algorithm_update': 0.0039043869135863144, 'loss': 0.0252154201905678, 'time_step': 0.004103558481690009, 'init_value': -1.6846381425857544, 'ave_value': -1.6848610164560085, 'soft_opc': nan} step=9658\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.18 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_9658.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a197d2cec2e54ec387f98857baedd86b",
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      },
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.23 [info     ] FQE_20220416023035: epoch=12 step=10536 epoch=12 metrics={'time_sample_batch': 0.00013890950717795683, 'time_algorithm_update': 0.003996203863539294, 'loss': 0.02730591854972219, 'time_step': 0.004192780525103246, 'init_value': -1.7097747325897217, 'ave_value': -1.7100223125838854, 'soft_opc': nan} step=10536\n"
     ]
    },
    {
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.23 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_10536.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d6e57d8138fc481e81b022c39d48efc4",
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.28 [info     ] FQE_20220416023035: epoch=13 step=11414 epoch=13 metrics={'time_sample_batch': 0.00014992862736174078, 'time_algorithm_update': 0.004310467520172884, 'loss': 0.02973489029262921, 'time_step': 0.004526891306483935, 'init_value': -1.7842313051223755, 'ave_value': -1.7845746293989986, 'soft_opc': nan} step=11414\n"
     ]
    },
    {
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.28 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_11414.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3fcd6d4e0f4041219d5277b929233fcc",
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.32 [info     ] FQE_20220416023035: epoch=14 step=12292 epoch=14 metrics={'time_sample_batch': 0.0001497428889698211, 'time_algorithm_update': 0.0044063218362239065, 'loss': 0.031877272707285076, 'time_step': 0.004620766856947358, 'init_value': -1.86216402053833, 'ave_value': -1.8628361398188722, 'soft_opc': nan} step=12292\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.32 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_12292.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e6ac438f954494dbe223c50233e9f38",
       "version_major": 2,
       "version_minor": 0
      },
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     "metadata": {},
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.37 [info     ] FQE_20220416023035: epoch=15 step=13170 epoch=15 metrics={'time_sample_batch': 0.00013555644041856493, 'time_algorithm_update': 0.003925226815469173, 'loss': 0.03220105893429627, 'time_step': 0.00411816881566493, 'init_value': -1.7957780361175537, 'ave_value': -1.7962395659541448, 'soft_opc': nan} step=13170\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.37 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_13170.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d7db26f851f496fa7aff5cb1caf96be",
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     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.41 [info     ] FQE_20220416023035: epoch=16 step=14048 epoch=16 metrics={'time_sample_batch': 0.00013657827312147972, 'time_algorithm_update': 0.003964018170002653, 'loss': 0.030373404652768353, 'time_step': 0.0041586896553126446, 'init_value': -1.7345776557922363, 'ave_value': -1.7352293853543346, 'soft_opc': nan} step=14048\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.41 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_14048.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee2619f0ce0b4067a7be07c15086b291",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.46 [info     ] FQE_20220416023035: epoch=17 step=14926 epoch=17 metrics={'time_sample_batch': 0.00014634909010693804, 'time_algorithm_update': 0.004316313934760648, 'loss': 0.02886914298745339, 'time_step': 0.004524589942636686, 'init_value': -1.7337546348571777, 'ave_value': -1.7345796636725788, 'soft_opc': nan} step=14926\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.46 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_14926.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab321463912a4227bf429575b685d29c",
       "version_major": 2,
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      },
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.51 [info     ] FQE_20220416023035: epoch=18 step=15804 epoch=18 metrics={'time_sample_batch': 0.000145303904331355, 'time_algorithm_update': 0.00420207694885671, 'loss': 0.030164917557960098, 'time_step': 0.004408499917571257, 'init_value': -1.812066674232483, 'ave_value': -1.812928592132238, 'soft_opc': nan} step=15804\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.51 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_15804.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "919de817bf8a45a8aacc9f31aa78a78f",
       "version_major": 2,
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.56 [info     ] FQE_20220416023035: epoch=19 step=16682 epoch=19 metrics={'time_sample_batch': 0.00013428505569492767, 'time_algorithm_update': 0.0038951179161158672, 'loss': 0.0315289858962116, 'time_step': 0.004085287960895374, 'init_value': -1.8328897953033447, 'ave_value': -1.8337049069555578, 'soft_opc': nan} step=16682\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:31.56 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_16682.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "486c9815a5194035a16c7ac608fa210b",
       "version_major": 2,
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      },
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.01 [info     ] FQE_20220416023035: epoch=20 step=17560 epoch=20 metrics={'time_sample_batch': 0.00014040573311286528, 'time_algorithm_update': 0.004186181924337678, 'loss': 0.03234182231752208, 'time_step': 0.004386157544164288, 'init_value': -1.8282415866851807, 'ave_value': -1.8289167450547472, 'soft_opc': nan} step=17560\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.01 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_17560.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1f3c246078064d8c9b441526d06e1838",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.05 [info     ] FQE_20220416023035: epoch=21 step=18438 epoch=21 metrics={'time_sample_batch': 0.00013677405876559386, 'time_algorithm_update': 0.0040657305771777735, 'loss': 0.031840031128355796, 'time_step': 0.004261241415367039, 'init_value': -1.8357875347137451, 'ave_value': -1.8365463098278998, 'soft_opc': nan} step=18438\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.05 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_18438.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "efef6a12384f4bb08bd9ef39858dda14",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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    {
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.10 [info     ] FQE_20220416023035: epoch=22 step=19316 epoch=22 metrics={'time_sample_batch': 0.00014043098701702978, 'time_algorithm_update': 0.004209822565656588, 'loss': 0.0324509807861724, 'time_step': 0.004413044534131444, 'init_value': -1.864782452583313, 'ave_value': -1.8655394506221201, 'soft_opc': nan} step=19316\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.10 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_19316.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "297a8b47330e43589cfeec37076dd91b",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
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    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.15 [info     ] FQE_20220416023035: epoch=23 step=20194 epoch=23 metrics={'time_sample_batch': 0.00014109274792508276, 'time_algorithm_update': 0.004184769334988605, 'loss': 0.032868608866862103, 'time_step': 0.004385049359401972, 'init_value': -1.8383742570877075, 'ave_value': -1.8393810563325699, 'soft_opc': nan} step=20194\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.15 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_20194.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2c7d63a77864663902500f80b850eeb",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.19 [info     ] FQE_20220416023035: epoch=24 step=21072 epoch=24 metrics={'time_sample_batch': 0.00014618996335596593, 'time_algorithm_update': 0.004358206903201301, 'loss': 0.03188741395750761, 'time_step': 0.004569656485034013, 'init_value': -1.809640645980835, 'ave_value': -1.810712363120897, 'soft_opc': nan} step=21072\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.19 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_21072.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08518ef57e734952a02146ac90014cf0",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.24 [info     ] FQE_20220416023035: epoch=25 step=21950 epoch=25 metrics={'time_sample_batch': 0.00014463834176030953, 'time_algorithm_update': 0.004326202060204161, 'loss': 0.03047883990812125, 'time_step': 0.004530911565098513, 'init_value': -1.7431156635284424, 'ave_value': -1.7441894749333289, 'soft_opc': nan} step=21950\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.24 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_21950.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ff748dd8ac4426a8099b7d9d32af0eb",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.29 [info     ] FQE_20220416023035: epoch=26 step=22828 epoch=26 metrics={'time_sample_batch': 0.00014882695973598333, 'time_algorithm_update': 0.004618687347290456, 'loss': 0.0275054973336132, 'time_step': 0.004828963029900553, 'init_value': -1.6903009414672852, 'ave_value': -1.6911078343957524, 'soft_opc': nan} step=22828\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.29 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_22828.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e5256b23fad4fa18cc42694a5d31cca",
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.34 [info     ] FQE_20220416023035: epoch=27 step=23706 epoch=27 metrics={'time_sample_batch': 0.00014512522617070713, 'time_algorithm_update': 0.00448395152298356, 'loss': 0.028326731827074417, 'time_step': 0.0046899834635045915, 'init_value': -1.7107409238815308, 'ave_value': -1.711418800133264, 'soft_opc': nan} step=23706\n"
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.34 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_23706.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4400f4741b404952bb250b785172d145",
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     "text": [
      "2022-04-16 02:32.39 [info     ] FQE_20220416023035: epoch=28 step=24584 epoch=28 metrics={'time_sample_batch': 0.00014933882650318883, 'time_algorithm_update': 0.004580956656731886, 'loss': 0.030346161860706905, 'time_step': 0.004792011951802803, 'init_value': -1.7637284994125366, 'ave_value': -1.7644309711968191, 'soft_opc': nan} step=24584\n"
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    },
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.39 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_24584.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a68bac37d2d47e7b1c46deb5b14c021",
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      "2022-04-16 02:32.44 [info     ] FQE_20220416023035: epoch=29 step=25462 epoch=29 metrics={'time_sample_batch': 0.00013869145465060084, 'time_algorithm_update': 0.004145365641165974, 'loss': 0.030744309375907365, 'time_step': 0.004343791811775781, 'init_value': -1.7662991285324097, 'ave_value': -1.7669989213987976, 'soft_opc': nan} step=25462\n"
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.44 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_25462.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5aaba3bd04542dda8502d61ce951014",
       "version_major": 2,
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.49 [info     ] FQE_20220416023035: epoch=30 step=26340 epoch=30 metrics={'time_sample_batch': 0.00015354428041497234, 'time_algorithm_update': 0.004875266470507228, 'loss': 0.02957598905034114, 'time_step': 0.005093423000500794, 'init_value': -1.7704755067825317, 'ave_value': -1.7712099605395633, 'soft_opc': nan} step=26340\n"
     ]
    },
    {
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     "output_type": "stream",
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      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.49 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_26340.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1d5c286faca4d2e83ea456e43987e24",
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     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.54 [info     ] FQE_20220416023035: epoch=31 step=27218 epoch=31 metrics={'time_sample_batch': 0.00014105934760022, 'time_algorithm_update': 0.00428303770703987, 'loss': 0.029563225869690277, 'time_step': 0.004484878585659018, 'init_value': -1.7354005575180054, 'ave_value': -1.7362601257036867, 'soft_opc': nan} step=27218\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.54 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_27218.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b62e8248a6694ac5b31cf2e79e4b025c",
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     },
     "metadata": {},
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.59 [info     ] FQE_20220416023035: epoch=32 step=28096 epoch=32 metrics={'time_sample_batch': 0.00014556649062519465, 'time_algorithm_update': 0.004472258150713319, 'loss': 0.028671632226459947, 'time_step': 0.004681357490174591, 'init_value': -1.7392584085464478, 'ave_value': -1.739950036772536, 'soft_opc': nan} step=28096\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:32.59 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_28096.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "24bd1d0fd7e6468892538b58f487e4c8",
       "version_major": 2,
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     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.03 [info     ] FQE_20220416023035: epoch=33 step=28974 epoch=33 metrics={'time_sample_batch': 0.0001400893804424173, 'time_algorithm_update': 0.004213993804601439, 'loss': 0.02879818527986191, 'time_step': 0.004412340140288403, 'init_value': -1.750124216079712, 'ave_value': -1.7508753112040985, 'soft_opc': nan} step=28974\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.03 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_28974.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2a2e5b1c164f4791941979be435b451a",
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      },
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.08 [info     ] FQE_20220416023035: epoch=34 step=29852 epoch=34 metrics={'time_sample_batch': 0.0001476870039329442, 'time_algorithm_update': 0.004615496122755603, 'loss': 0.029663681985756384, 'time_step': 0.004829249783909131, 'init_value': -1.7925469875335693, 'ave_value': -1.7931977181664227, 'soft_opc': nan} step=29852\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.08 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_29852.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b119a7b166c64fec966a9cc2b53e3481",
       "version_major": 2,
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.14 [info     ] FQE_20220416023035: epoch=35 step=30730 epoch=35 metrics={'time_sample_batch': 0.00015107808732226118, 'time_algorithm_update': 0.004729251655196274, 'loss': 0.03169065988115091, 'time_step': 0.004944545261409125, 'init_value': -1.8089278936386108, 'ave_value': -1.809744958812335, 'soft_opc': nan} step=30730\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.14 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_30730.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d8eab1f73c15433c8c31c9ad3de2a20d",
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       "version_minor": 0
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     "metadata": {},
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     "text": [
      "2022-04-16 02:33.19 [info     ] FQE_20220416023035: epoch=36 step=31608 epoch=36 metrics={'time_sample_batch': 0.00014640584350446905, 'time_algorithm_update': 0.004500647612204584, 'loss': 0.03188671774939139, 'time_step': 0.004708938012090522, 'init_value': -1.8292759656906128, 'ave_value': -1.830096120219216, 'soft_opc': nan} step=31608\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.19 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_31608.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d96b551bd1a345e4bc7562a73b13285e",
       "version_major": 2,
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     },
     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.24 [info     ] FQE_20220416023035: epoch=37 step=32486 epoch=37 metrics={'time_sample_batch': 0.00013965409002977515, 'time_algorithm_update': 0.004309186902839122, 'loss': 0.033639269637101246, 'time_step': 0.004508757102462317, 'init_value': -1.911298155784607, 'ave_value': -1.91226389748077, 'soft_opc': nan} step=32486\n"
     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.24 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_32486.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6084db8c4d9144fda72b7377555fc905",
       "version_major": 2,
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      },
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     },
     "metadata": {},
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.28 [info     ] FQE_20220416023035: epoch=38 step=33364 epoch=38 metrics={'time_sample_batch': 0.0001458187581194832, 'time_algorithm_update': 0.004480876520717334, 'loss': 0.03275476183350846, 'time_step': 0.004687369548649885, 'init_value': -1.8340646028518677, 'ave_value': -1.8349596584880514, 'soft_opc': nan} step=33364\n"
     ]
    },
    {
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     "output_type": "stream",
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.28 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_33364.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5936dc8c8a6e44278909575c6362d531",
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     "metadata": {},
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.33 [info     ] FQE_20220416023035: epoch=39 step=34242 epoch=39 metrics={'time_sample_batch': 0.0001397765578876052, 'time_algorithm_update': 0.004271186294208084, 'loss': 0.03299221052990315, 'time_step': 0.0044703258197236985, 'init_value': -1.8769742250442505, 'ave_value': -1.8779120190601373, 'soft_opc': nan} step=34242\n"
     ]
    },
    {
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.33 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_34242.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "42934bac7e194b32af60e8b73e0120e7",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.38 [info     ] FQE_20220416023035: epoch=40 step=35120 epoch=40 metrics={'time_sample_batch': 0.00014861841136610862, 'time_algorithm_update': 0.004568259373884266, 'loss': 0.033787317322510585, 'time_step': 0.00478131298595246, 'init_value': -1.8500256538391113, 'ave_value': -1.8510389510944938, 'soft_opc': nan} step=35120\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.38 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_35120.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ca88c5c61b804368818c23c0b6bbd94d",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.43 [info     ] FQE_20220416023035: epoch=41 step=35998 epoch=41 metrics={'time_sample_batch': 0.00014751402826678508, 'time_algorithm_update': 0.00457637836829948, 'loss': 0.032868853872077455, 'time_step': 0.004788737633776827, 'init_value': -1.838673710823059, 'ave_value': -1.8396480957839823, 'soft_opc': nan} step=35998\n"
     ]
    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.43 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_35998.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5979140f16d4e35be3eb522f59ec3c4",
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     "text": [
      "2022-04-16 02:33.48 [info     ] FQE_20220416023035: epoch=42 step=36876 epoch=42 metrics={'time_sample_batch': 0.00013275841645607917, 'time_algorithm_update': 0.0039938780604299486, 'loss': 0.032860633971842725, 'time_step': 0.0041831970757938465, 'init_value': -1.8601897954940796, 'ave_value': -1.8611401822810296, 'soft_opc': nan} step=36876\n"
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    {
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.48 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_36876.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7cad470a0e5f427b9c2c75b83f2a04f5",
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     "text": [
      "2022-04-16 02:33.52 [info     ] FQE_20220416023035: epoch=43 step=37754 epoch=43 metrics={'time_sample_batch': 0.0001437634161773195, 'time_algorithm_update': 0.00453113749249921, 'loss': 0.03276707478193376, 'time_step': 0.0047353419193102726, 'init_value': -1.8805840015411377, 'ave_value': -1.8815401830648164, 'soft_opc': nan} step=37754\n"
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     "text": [
      "2022-04-16 02:33.52 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_37754.pt\n"
     ]
    },
    {
     "data": {
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       "model_id": "96397485ce35441eafa049e89c9098f2",
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     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.58 [info     ] FQE_20220416023035: epoch=44 step=38632 epoch=44 metrics={'time_sample_batch': 0.00015553988193868232, 'time_algorithm_update': 0.004855139108888109, 'loss': 0.031820492851487796, 'time_step': 0.005076848836042886, 'init_value': -1.8011033535003662, 'ave_value': -1.8020631308334647, 'soft_opc': nan} step=38632\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:33.58 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_38632.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5648e2ee85647f1a03df065c78ac513",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 45/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.03 [info     ] FQE_20220416023035: epoch=45 step=39510 epoch=45 metrics={'time_sample_batch': 0.0001458649211701065, 'time_algorithm_update': 0.004554569313900889, 'loss': 0.029658214711368803, 'time_step': 0.004763913860625178, 'init_value': -1.7536741495132446, 'ave_value': -1.7545088305067054, 'soft_opc': nan} step=39510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.03 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_39510.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "696071dd86454a1db9ca61fe3427c374",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 46/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.08 [info     ] FQE_20220416023035: epoch=46 step=40388 epoch=46 metrics={'time_sample_batch': 0.00014555671492035677, 'time_algorithm_update': 0.004380924826602458, 'loss': 0.029396214036720113, 'time_step': 0.004588917067490841, 'init_value': -1.767454743385315, 'ave_value': -1.7680199972479629, 'soft_opc': nan} step=40388\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.08 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_40388.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5840be786b848e7be7c70664048dec4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 47/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.13 [info     ] FQE_20220416023035: epoch=47 step=41266 epoch=47 metrics={'time_sample_batch': 0.0001473166133385313, 'time_algorithm_update': 0.004525725825229376, 'loss': 0.02933070735589395, 'time_step': 0.004734723605979276, 'init_value': -1.7582861185073853, 'ave_value': -1.7588222786023489, 'soft_opc': nan} step=41266\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.13 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_41266.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff20f74d788b4dd1ae1a33620fbfc20e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 48/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.17 [info     ] FQE_20220416023035: epoch=48 step=42144 epoch=48 metrics={'time_sample_batch': 0.0001388313015392538, 'time_algorithm_update': 0.004317575272232091, 'loss': 0.0289198401462095, 'time_step': 0.00451718756169556, 'init_value': -1.7919765710830688, 'ave_value': -1.7926710625999824, 'soft_opc': nan} step=42144\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.17 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_42144.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac0300c07cf44802bb9ab9634baa99b0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 49/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.22 [info     ] FQE_20220416023035: epoch=49 step=43022 epoch=49 metrics={'time_sample_batch': 0.0001453994890008809, 'time_algorithm_update': 0.00469416692208049, 'loss': 0.030152450756191034, 'time_step': 0.004901665761421916, 'init_value': -1.7787705659866333, 'ave_value': -1.7795009903001273, 'soft_opc': nan} step=43022\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.22 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_43022.pt\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "987a996b5be34a88ace64124d7bc9565",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Epoch 50/50:   0%|          | 0/878 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.28 [info     ] FQE_20220416023035: epoch=50 step=43900 epoch=50 metrics={'time_sample_batch': 0.0001511989258959516, 'time_algorithm_update': 0.00497674344612417, 'loss': 0.029767772812324636, 'time_step': 0.005192360193691384, 'init_value': -1.7842823266983032, 'ave_value': -1.7847246024878274, 'soft_opc': nan} step=43900\n"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:NaN or Inf found in input tensor.\n"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2022-04-16 02:34.28 [info     ] Model parameters are saved to d3rlpy_logs/FQE_20220416023035/model_43900.pt\n"
     ]
    },
    {
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       " (29,\n",
       "  {'time_sample_batch': 0.00013869145465060084,\n",
       "   'time_algorithm_update': 0.004145365641165974,\n",
       "   'loss': 0.030744309375907365,\n",
       "   'time_step': 0.004343791811775781,\n",
       "   'init_value': -1.7662991285324097,\n",
       "   'ave_value': -1.7669989213987976,\n",
       "   'soft_opc': nan}),\n",
       " (30,\n",
       "  {'time_sample_batch': 0.00015354428041497234,\n",
       "   'time_algorithm_update': 0.004875266470507228,\n",
       "   'loss': 0.02957598905034114,\n",
       "   'time_step': 0.005093423000500794,\n",
       "   'init_value': -1.7704755067825317,\n",
       "   'ave_value': -1.7712099605395633,\n",
       "   'soft_opc': nan}),\n",
       " (31,\n",
       "  {'time_sample_batch': 0.00014105934760022,\n",
       "   'time_algorithm_update': 0.00428303770703987,\n",
       "   'loss': 0.029563225869690277,\n",
       "   'time_step': 0.004484878585659018,\n",
       "   'init_value': -1.7354005575180054,\n",
       "   'ave_value': -1.7362601257036867,\n",
       "   'soft_opc': nan}),\n",
       " (32,\n",
       "  {'time_sample_batch': 0.00014556649062519465,\n",
       "   'time_algorithm_update': 0.004472258150713319,\n",
       "   'loss': 0.028671632226459947,\n",
       "   'time_step': 0.004681357490174591,\n",
       "   'init_value': -1.7392584085464478,\n",
       "   'ave_value': -1.739950036772536,\n",
       "   'soft_opc': nan}),\n",
       " (33,\n",
       "  {'time_sample_batch': 0.0001400893804424173,\n",
       "   'time_algorithm_update': 0.004213993804601439,\n",
       "   'loss': 0.02879818527986191,\n",
       "   'time_step': 0.004412340140288403,\n",
       "   'init_value': -1.750124216079712,\n",
       "   'ave_value': -1.7508753112040985,\n",
       "   'soft_opc': nan}),\n",
       " (34,\n",
       "  {'time_sample_batch': 0.0001476870039329442,\n",
       "   'time_algorithm_update': 0.004615496122755603,\n",
       "   'loss': 0.029663681985756384,\n",
       "   'time_step': 0.004829249783909131,\n",
       "   'init_value': -1.7925469875335693,\n",
       "   'ave_value': -1.7931977181664227,\n",
       "   'soft_opc': nan}),\n",
       " (35,\n",
       "  {'time_sample_batch': 0.00015107808732226118,\n",
       "   'time_algorithm_update': 0.004729251655196274,\n",
       "   'loss': 0.03169065988115091,\n",
       "   'time_step': 0.004944545261409125,\n",
       "   'init_value': -1.8089278936386108,\n",
       "   'ave_value': -1.809744958812335,\n",
       "   'soft_opc': nan}),\n",
       " (36,\n",
       "  {'time_sample_batch': 0.00014640584350446905,\n",
       "   'time_algorithm_update': 0.004500647612204584,\n",
       "   'loss': 0.03188671774939139,\n",
       "   'time_step': 0.004708938012090522,\n",
       "   'init_value': -1.8292759656906128,\n",
       "   'ave_value': -1.830096120219216,\n",
       "   'soft_opc': nan}),\n",
       " (37,\n",
       "  {'time_sample_batch': 0.00013965409002977515,\n",
       "   'time_algorithm_update': 0.004309186902839122,\n",
       "   'loss': 0.033639269637101246,\n",
       "   'time_step': 0.004508757102462317,\n",
       "   'init_value': -1.911298155784607,\n",
       "   'ave_value': -1.91226389748077,\n",
       "   'soft_opc': nan}),\n",
       " (38,\n",
       "  {'time_sample_batch': 0.0001458187581194832,\n",
       "   'time_algorithm_update': 0.004480876520717334,\n",
       "   'loss': 0.03275476183350846,\n",
       "   'time_step': 0.004687369548649885,\n",
       "   'init_value': -1.8340646028518677,\n",
       "   'ave_value': -1.8349596584880514,\n",
       "   'soft_opc': nan}),\n",
       " (39,\n",
       "  {'time_sample_batch': 0.0001397765578876052,\n",
       "   'time_algorithm_update': 0.004271186294208084,\n",
       "   'loss': 0.03299221052990315,\n",
       "   'time_step': 0.0044703258197236985,\n",
       "   'init_value': -1.8769742250442505,\n",
       "   'ave_value': -1.8779120190601373,\n",
       "   'soft_opc': nan}),\n",
       " (40,\n",
       "  {'time_sample_batch': 0.00014861841136610862,\n",
       "   'time_algorithm_update': 0.004568259373884266,\n",
       "   'loss': 0.033787317322510585,\n",
       "   'time_step': 0.00478131298595246,\n",
       "   'init_value': -1.8500256538391113,\n",
       "   'ave_value': -1.8510389510944938,\n",
       "   'soft_opc': nan}),\n",
       " (41,\n",
       "  {'time_sample_batch': 0.00014751402826678508,\n",
       "   'time_algorithm_update': 0.00457637836829948,\n",
       "   'loss': 0.032868853872077455,\n",
       "   'time_step': 0.004788737633776827,\n",
       "   'init_value': -1.838673710823059,\n",
       "   'ave_value': -1.8396480957839823,\n",
       "   'soft_opc': nan}),\n",
       " (42,\n",
       "  {'time_sample_batch': 0.00013275841645607917,\n",
       "   'time_algorithm_update': 0.0039938780604299486,\n",
       "   'loss': 0.032860633971842725,\n",
       "   'time_step': 0.0041831970757938465,\n",
       "   'init_value': -1.8601897954940796,\n",
       "   'ave_value': -1.8611401822810296,\n",
       "   'soft_opc': nan}),\n",
       " (43,\n",
       "  {'time_sample_batch': 0.0001437634161773195,\n",
       "   'time_algorithm_update': 0.00453113749249921,\n",
       "   'loss': 0.03276707478193376,\n",
       "   'time_step': 0.0047353419193102726,\n",
       "   'init_value': -1.8805840015411377,\n",
       "   'ave_value': -1.8815401830648164,\n",
       "   'soft_opc': nan}),\n",
       " (44,\n",
       "  {'time_sample_batch': 0.00015553988193868232,\n",
       "   'time_algorithm_update': 0.004855139108888109,\n",
       "   'loss': 0.031820492851487796,\n",
       "   'time_step': 0.005076848836042886,\n",
       "   'init_value': -1.8011033535003662,\n",
       "   'ave_value': -1.8020631308334647,\n",
       "   'soft_opc': nan}),\n",
       " (45,\n",
       "  {'time_sample_batch': 0.0001458649211701065,\n",
       "   'time_algorithm_update': 0.004554569313900889,\n",
       "   'loss': 0.029658214711368803,\n",
       "   'time_step': 0.004763913860625178,\n",
       "   'init_value': -1.7536741495132446,\n",
       "   'ave_value': -1.7545088305067054,\n",
       "   'soft_opc': nan}),\n",
       " (46,\n",
       "  {'time_sample_batch': 0.00014555671492035677,\n",
       "   'time_algorithm_update': 0.004380924826602458,\n",
       "   'loss': 0.029396214036720113,\n",
       "   'time_step': 0.004588917067490841,\n",
       "   'init_value': -1.767454743385315,\n",
       "   'ave_value': -1.7680199972479629,\n",
       "   'soft_opc': nan}),\n",
       " (47,\n",
       "  {'time_sample_batch': 0.0001473166133385313,\n",
       "   'time_algorithm_update': 0.004525725825229376,\n",
       "   'loss': 0.02933070735589395,\n",
       "   'time_step': 0.004734723605979276,\n",
       "   'init_value': -1.7582861185073853,\n",
       "   'ave_value': -1.7588222786023489,\n",
       "   'soft_opc': nan}),\n",
       " (48,\n",
       "  {'time_sample_batch': 0.0001388313015392538,\n",
       "   'time_algorithm_update': 0.004317575272232091,\n",
       "   'loss': 0.0289198401462095,\n",
       "   'time_step': 0.00451718756169556,\n",
       "   'init_value': -1.7919765710830688,\n",
       "   'ave_value': -1.7926710625999824,\n",
       "   'soft_opc': nan}),\n",
       " (49,\n",
       "  {'time_sample_batch': 0.0001453994890008809,\n",
       "   'time_algorithm_update': 0.00469416692208049,\n",
       "   'loss': 0.030152450756191034,\n",
       "   'time_step': 0.004901665761421916,\n",
       "   'init_value': -1.7787705659866333,\n",
       "   'ave_value': -1.7795009903001273,\n",
       "   'soft_opc': nan}),\n",
       " (50,\n",
       "  {'time_sample_batch': 0.0001511989258959516,\n",
       "   'time_algorithm_update': 0.00497674344612417,\n",
       "   'loss': 0.029767772812324636,\n",
       "   'time_step': 0.005192360193691384,\n",
       "   'init_value': -1.7842823266983032,\n",
       "   'ave_value': -1.7847246024878274,\n",
       "   'soft_opc': nan})]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from d3rlpy.ope import FQE\n",
    "# metrics to evaluate with\n",
    "from d3rlpy.metrics.scorer import soft_opc_scorer\n",
    "\n",
    "\n",
    "ope_dataset, states_ope, actions_ope, rewards_ope = get_dataset([2,4,6,8], path=\"collected_data/rl_deterministic.txt\") #change if you'd prefer different chunks\n",
    "ope_train_episodes, ope_test_episodes = train_test_split(ope_dataset, test_size=0.2)\n",
    "\n",
    "fqe = FQE(algo=model, action_scaler = action_scaler, use_gpu=False) #change this if you have one!\n",
    "fqe.fit(ope_train_episodes,\n",
    "        eval_episodes=ope_test_episodes,\n",
    "        tensorboard_dir='runs',\n",
    "        n_epochs=50, n_steps_per_epoch=1000, #change if overfitting/underfitting\n",
    "        scorers={\n",
    "           'init_value': initial_state_value_estimation_scorer,\n",
    "            'ave_value': average_value_estimation_scorer,\n",
    "           'soft_opc': soft_opc_scorer(return_threshold=0)\n",
    "        })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "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, states_ope, actions_ope, rewards_ope = get_dataset([2,4,6,8], path=\"collected_data/rl_stoch_small.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=50, n_steps_per_epoch=1000, #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",
    "#         })"
   ]
  }
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