a2c for trade-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "import gym\n",
    "import gym_anytrading\n",
    "from stable_baselines3 import A2C\n",
    "from stable_baselines3.common.vec_env import DummyVecEnv\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "key_id = 'PK04UHV69AF2QULV4REU'\n",
    "secret_key = '7g1qUN7qjsfW3U6oSccYYtHyQdoewJ12ANDQmSKd'\n",
    "endpoint = 'https://paper-api.alpaca.markets'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect to alpaca api\n",
    "from alpaca.data.historical import StockHistoricalDataClient\n",
    "from alpaca.data.requests import StockBarsRequest\n",
    "from alpaca.data.timeframe import TimeFrame\n",
    "from datetime import datetime\n",
    "\n",
    "\n",
    "stock_client = StockHistoricalDataClient(key_id, secret_key)\n",
    "\n",
    "# get 1 years worth data on Nvidia by the day. Could also use TimeFrame.Minute for daily data\n",
    "request_params = StockBarsRequest(symbol_or_symbols=['NVDA'],\n",
    "                                  timeframe=TimeFrame.Day,\n",
    "                                  start=datetime(2020, 1, 1),\n",
    "                                  end=datetime(2022, 1, 1)\n",
    "                                  )\n",
    "\n",
    "bars = stock_client.get_stock_bars(request_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>trade_count</th>\n",
       "      <th>vwap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02 05:00:00+00:00</td>\n",
       "      <td>238.85</td>\n",
       "      <td>239.91</td>\n",
       "      <td>236.720</td>\n",
       "      <td>239.91</td>\n",
       "      <td>5941969.0</td>\n",
       "      <td>67912.0</td>\n",
       "      <td>238.723319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03 05:00:00+00:00</td>\n",
       "      <td>235.00</td>\n",
       "      <td>237.83</td>\n",
       "      <td>234.100</td>\n",
       "      <td>236.07</td>\n",
       "      <td>5145208.0</td>\n",
       "      <td>63605.0</td>\n",
       "      <td>236.384392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06 05:00:00+00:00</td>\n",
       "      <td>232.36</td>\n",
       "      <td>237.27</td>\n",
       "      <td>231.270</td>\n",
       "      <td>237.06</td>\n",
       "      <td>6572812.0</td>\n",
       "      <td>73912.0</td>\n",
       "      <td>235.468778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07 05:00:00+00:00</td>\n",
       "      <td>238.20</td>\n",
       "      <td>241.77</td>\n",
       "      <td>236.390</td>\n",
       "      <td>239.93</td>\n",
       "      <td>7980144.0</td>\n",
       "      <td>81624.0</td>\n",
       "      <td>239.855220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08 05:00:00+00:00</td>\n",
       "      <td>239.76</td>\n",
       "      <td>242.04</td>\n",
       "      <td>238.149</td>\n",
       "      <td>240.38</td>\n",
       "      <td>6931017.0</td>\n",
       "      <td>70240.0</td>\n",
       "      <td>240.313148</td>\n",
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      ],
      "text/plain": [
       "                  timestamp    open    high      low   close     volume  \\\n",
       "0 2020-01-02 05:00:00+00:00  238.85  239.91  236.720  239.91  5941969.0   \n",
       "1 2020-01-03 05:00:00+00:00  235.00  237.83  234.100  236.07  5145208.0   \n",
       "2 2020-01-06 05:00:00+00:00  232.36  237.27  231.270  237.06  6572812.0   \n",
       "3 2020-01-07 05:00:00+00:00  238.20  241.77  236.390  239.93  7980144.0   \n",
       "4 2020-01-08 05:00:00+00:00  239.76  242.04  238.149  240.38  6931017.0   \n",
       "\n",
       "   trade_count        vwap  \n",
       "0      67912.0  238.723319  \n",
       "1      63605.0  236.384392  \n",
       "2      73912.0  235.468778  \n",
       "3      81624.0  239.855220  \n",
       "4      70240.0  240.313148  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=bars.df.reset_index()\n",
    "data.head()\n",
    "df=data.iloc[:,1:]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp      datetime64[ns, UTC]\n",
       "open                       float64\n",
       "high                       float64\n",
       "low                        float64\n",
       "close                      float64\n",
       "volume                     float64\n",
       "trade_count                float64\n",
       "vwap                       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>Close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02 05:00:00+00:00</td>\n",
       "      <td>238.85</td>\n",
       "      <td>239.91</td>\n",
       "      <td>236.720</td>\n",
       "      <td>239.91</td>\n",
       "      <td>5941969.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03 05:00:00+00:00</td>\n",
       "      <td>235.00</td>\n",
       "      <td>237.83</td>\n",
       "      <td>234.100</td>\n",
       "      <td>236.07</td>\n",
       "      <td>5145208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06 05:00:00+00:00</td>\n",
       "      <td>232.36</td>\n",
       "      <td>237.27</td>\n",
       "      <td>231.270</td>\n",
       "      <td>237.06</td>\n",
       "      <td>6572812.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07 05:00:00+00:00</td>\n",
       "      <td>238.20</td>\n",
       "      <td>241.77</td>\n",
       "      <td>236.390</td>\n",
       "      <td>239.93</td>\n",
       "      <td>7980144.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08 05:00:00+00:00</td>\n",
       "      <td>239.76</td>\n",
       "      <td>242.04</td>\n",
       "      <td>238.149</td>\n",
       "      <td>240.38</td>\n",
       "      <td>6931017.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Date    open    high      low   Close     volume\n",
       "0 2020-01-02 05:00:00+00:00  238.85  239.91  236.720  239.91  5941969.0\n",
       "1 2020-01-03 05:00:00+00:00  235.00  237.83  234.100  236.07  5145208.0\n",
       "2 2020-01-06 05:00:00+00:00  232.36  237.27  231.270  237.06  6572812.0\n",
       "3 2020-01-07 05:00:00+00:00  238.20  241.77  236.390  239.93  7980144.0\n",
       "4 2020-01-08 05:00:00+00:00  239.76  242.04  238.149  240.38  6931017.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from numpy import column_stack\n",
    "\n",
    "\n",
    "df.rename(columns={\"timestamp\":\"Date\",\"close\":'Close'},inplace=True)\n",
    "df=df.iloc[:,:-2]\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make('stocks-v0',df=df,frame_bound=(5,250),window_size=5)\n",
    "#env = gym.make('stocks-v0',frame_bound=(10,100),window_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "       [ 4.16860e+02,  9.08000e+00],\n",
       "       [ 4.08620e+02, -8.24000e+00],\n",
       "       [ 4.18620e+02,  1.00000e+01],\n",
       "       [ 4.24560e+02,  5.94000e+00],\n",
       "       [ 4.24590e+02,  3.00000e-02],\n",
       "       [ 4.40410e+02,  1.58200e+01],\n",
       "       [ 4.49110e+02,  8.70000e+00],\n",
       "       [ 4.51470e+02,  2.36000e+00],\n",
       "       [ 4.53420e+02,  1.95000e+00],\n",
       "       [ 4.47980e+02, -5.44000e+00],\n",
       "       [ 4.46600e+02, -1.38000e+00],\n",
       "       [ 4.34000e+02, -1.26000e+01],\n",
       "       [ 4.57610e+02,  2.36100e+01],\n",
       "       [ 4.57720e+02,  1.10000e-01],\n",
       "       [ 4.62560e+02,  4.84000e+00],\n",
       "       [ 4.93480e+02,  3.09200e+01],\n",
       "       [ 4.90430e+02, -3.05000e+00],\n",
       "       [ 4.85540e+02, -4.89000e+00],\n",
       "       [ 4.85640e+02,  1.00000e-01],\n",
       "       [ 5.07340e+02,  2.17000e+01],\n",
       "       [ 5.08810e+02,  1.47000e+00],\n",
       "       [ 5.10000e+02,  1.19000e+00],\n",
       "       [ 5.10920e+02,  9.20000e-01],\n",
       "       [ 5.05130e+02, -5.79000e+00],\n",
       "       [ 5.25910e+02,  2.07800e+01],\n",
       "       [ 5.34980e+02,  9.07000e+00],\n",
       "       [ 5.52840e+02,  1.78600e+01],\n",
       "       [ 5.73860e+02,  2.10200e+01],\n",
       "       [ 5.20620e+02, -5.32400e+01],\n",
       "       [ 5.04900e+02, -1.57200e+01],\n",
       "       [ 4.76520e+02, -2.83800e+01],\n",
       "       [ 5.08600e+02,  3.20800e+01],\n",
       "       [ 4.92470e+02, -1.61300e+01],\n",
       "       [ 4.86580e+02, -5.89000e+00],\n",
       "       [ 5.14890e+02,  2.83100e+01],\n",
       "       [ 5.19640e+02,  4.75000e+00],\n",
       "       [ 5.00580e+02, -1.90600e+01],\n",
       "       [ 4.98540e+02, -2.04000e+00],\n",
       "       [ 4.87570e+02, -1.09700e+01],\n",
       "       [ 5.00690e+02,  1.31200e+01],\n",
       "       [ 5.05510e+02,  4.82000e+00],\n",
       "       [ 4.84950e+02, -2.05600e+01],\n",
       "       [ 4.93920e+02,  8.97000e+00],\n",
       "       [ 5.14950e+02,  2.10300e+01],\n",
       "       [ 5.21400e+02,  6.45000e+00],\n",
       "       [ 5.29030e+02,  7.63000e+00],\n",
       "       [ 5.41220e+02,  1.21900e+01],\n",
       "       [ 5.44580e+02,  3.36000e+00],\n",
       "       [ 5.22490e+02, -2.20900e+01],\n",
       "       [ 5.45700e+02,  2.32100e+01],\n",
       "       [ 5.49460e+02,  3.76000e+00],\n",
       "       [ 5.58560e+02,  9.10000e+00],\n",
       "       [ 5.53550e+02, -5.01000e+00],\n",
       "       [ 5.50510e+02, -3.04000e+00],\n",
       "       [ 5.69040e+02,  1.85300e+01],\n",
       "       [ 5.69930e+02,  8.90000e-01],\n",
       "       [ 5.63810e+02, -6.12000e+00],\n",
       "       [ 5.58800e+02, -5.01000e+00]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#env.signal_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([239.91 , 236.07 , 237.06 , 239.93 , 240.38 , 243.02 , 244.32 ,\n",
       "       251.98 , 247.28 , 245.57 , 248.93 , 249.28 , 247.94 , 250.05 ,\n",
       "       252.86 , 250.48 , 240.2  , 247.97 , 245.54 , 245.81 , 236.43 ,\n",
       "       240.33 , 247.13 , 250.76 , 254.245, 251.59 , 262.97 , 267.89 ,\n",
       "       272.54 , 270.78 , 289.79 , 296.57 , 314.7  , 308.7  , 294.07 ,\n",
       "       273.28 , 262.05 , 267.65 , 252.6  , 270.07 , 276.43 , 265.89 ,\n",
       "       284.51 , 273.29 , 266.04 , 245.44 , 261.08 , 246.47 , 216.31 ,\n",
       "       240.84 , 196.4  , 217.27 , 202.82 , 212.97 , 205.75 , 212.69 ,\n",
       "       249.18 , 245.62 , 257.24 , 252.73 , 265.59 , 263.6  , 243.07 ,\n",
       "       255.47 , 243.91 , 268.4  , 259.03 , 266.95 , 262.95 , 269.85 ,\n",
       "       283.95 , 280.84 , 294.7  , 292.32 , 287.05 , 269.51 , 286.15 ,\n",
       "       284.01 , 289.59 , 297.08 , 291.36 , 298.46 , 292.28 , 282.78 ,\n",
       "       291.285, 293.74 , 297.79 , 304.87 , 312.5  , 322.62 , 312.1  ,\n",
       "       311.2  , 321.22 , 339.63 , 350.01 , 352.22 , 358.8  , 351.01 ,\n",
       "       361.05 , 348.71 , 341.01 , 339.48 , 355.02 , 352.25 , 353.01 ,\n",
       "       350.78 , 350.66 , 356.8  , 352.2  , 361.84 , 374.67 , 351.85 ,\n",
       "       357.3  , 366.95 , 362.74 , 369.44 , 368.72 , 370.45 , 381.07 ,\n",
       "       378.   , 369.42 , 379.6  , 366.2  , 368.   , 379.91 , 381.2  ,\n",
       "       384.49 , 393.57 , 394.87 , 408.64 , 420.36 , 419.17 , 402.09 ,\n",
       "       415.08 , 409.09 , 405.39 , 408.06 , 420.43 , 413.14 , 417.55 ,\n",
       "       405.19 , 407.78 , 416.86 , 408.62 , 418.62 , 424.56 , 424.59 ,\n",
       "       440.41 , 449.11 , 451.47 , 453.42 , 447.98 , 446.6  , 434.   ,\n",
       "       457.61 , 457.72 , 462.56 , 493.48 , 490.43 , 485.54 , 485.64 ,\n",
       "       507.34 , 508.81 , 510.   , 510.92 , 505.13 , 525.91 , 534.98 ,\n",
       "       552.84 , 573.86 , 520.62 , 504.9  , 476.52 , 508.6  , 492.47 ,\n",
       "       486.58 , 514.89 , 519.64 , 500.58 , 498.54 , 487.57 , 500.69 ,\n",
       "       505.51 , 484.95 , 493.92 , 514.95 , 521.4  , 529.03 , 541.22 ,\n",
       "       544.58 , 522.49 , 545.7  , 549.46 , 558.56 , 553.55 , 550.51 ,\n",
       "       569.04 , 569.93 , 563.81 , 558.8  ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#env.prices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info: {'total_reward': -38.87000000000012, 'total_profit': 0.3228537402612443, 'position': 0}\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Build Environment\n",
    "state = env.reset()\n",
    "while True:\n",
    "    action = env.action_space.sample()\n",
    "    observation, reward, done, info = env.step(action)\n",
    "    # env.render()\n",
    "    if done:\n",
    "        print(\"info:\", info)\n",
    "        break\n",
    "\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.cla()\n",
    "env.render_all()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##Add Custom Indicators"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting finta\n",
      "  Downloading finta-1.3-py3-none-any.whl (29 kB)\n",
      "Requirement already satisfied: pandas in /Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages (from finta) (1.5.0)\n",
      "Requirement already satisfied: numpy in /Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages (from finta) (1.23.3)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages (from pandas->finta) (2021.3)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages (from pandas->finta) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/sampson/miniforge3/envs/tensorflow24/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas->finta) (1.16.0)\n",
      "Installing collected packages: finta\n",
      "Successfully installed finta-1.3\n",
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m22.3\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip3 install finta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gym_anytrading.envs import StocksEnv\n",
    "from finta import TA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['volume']=df['volume'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['volume']=df['volume'].apply(lambda x: float(x.replace(',','')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate the SMA RSI and OBV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>Close</th>\n",
       "      <th>volume</th>\n",
       "      <th>SMA</th>\n",
       "      <th>RSI</th>\n",
       "      <th>OBV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-02 05:00:00+00:00</td>\n",
       "      <td>238.85</td>\n",
       "      <td>239.9100</td>\n",
       "      <td>236.7200</td>\n",
       "      <td>239.91</td>\n",
       "      <td>5941969.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-03 05:00:00+00:00</td>\n",
       "      <td>235.00</td>\n",
       "      <td>237.8300</td>\n",
       "      <td>234.1000</td>\n",
       "      <td>236.07</td>\n",
       "      <td>5145208.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-5145208.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-06 05:00:00+00:00</td>\n",
       "      <td>232.36</td>\n",
       "      <td>237.2700</td>\n",
       "      <td>231.2700</td>\n",
       "      <td>237.06</td>\n",
       "      <td>6572812.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>21.730950</td>\n",
       "      <td>1427604.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-07 05:00:00+00:00</td>\n",
       "      <td>238.20</td>\n",
       "      <td>241.7700</td>\n",
       "      <td>236.3900</td>\n",
       "      <td>239.93</td>\n",
       "      <td>7980144.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.367920</td>\n",
       "      <td>9407748.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-08 05:00:00+00:00</td>\n",
       "      <td>239.76</td>\n",
       "      <td>242.0400</td>\n",
       "      <td>238.1490</td>\n",
       "      <td>240.38</td>\n",
       "      <td>6931017.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>56.347335</td>\n",
       "      <td>16338765.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-09 05:00:00+00:00</td>\n",
       "      <td>243.55</td>\n",
       "      <td>245.9297</td>\n",
       "      <td>240.8550</td>\n",
       "      <td>243.02</td>\n",
       "      <td>6386053.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.900960</td>\n",
       "      <td>22724818.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-01-10 05:00:00+00:00</td>\n",
       "      <td>247.48</td>\n",
       "      <td>248.5500</td>\n",
       "      <td>243.7500</td>\n",
       "      <td>244.32</td>\n",
       "      <td>7911560.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.016122</td>\n",
       "      <td>30636378.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-01-13 05:00:00+00:00</td>\n",
       "      <td>247.66</td>\n",
       "      <td>252.9884</td>\n",
       "      <td>246.7500</td>\n",
       "      <td>251.98</td>\n",
       "      <td>8008451.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>85.332239</td>\n",
       "      <td>38644829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-01-14 05:00:00+00:00</td>\n",
       "      <td>250.51</td>\n",
       "      <td>251.2800</td>\n",
       "      <td>246.7500</td>\n",
       "      <td>247.28</td>\n",
       "      <td>8989483.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.559779</td>\n",
       "      <td>29655346.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-01-15 05:00:00+00:00</td>\n",
       "      <td>247.79</td>\n",
       "      <td>248.6891</td>\n",
       "      <td>244.5300</td>\n",
       "      <td>245.57</td>\n",
       "      <td>6577646.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.103112</td>\n",
       "      <td>23077700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-01-16 05:00:00+00:00</td>\n",
       "      <td>247.63</td>\n",
       "      <td>249.1700</td>\n",
       "      <td>246.7750</td>\n",
       "      <td>248.93</td>\n",
       "      <td>7104791.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.077643</td>\n",
       "      <td>30182491.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-01-17 05:00:00+00:00</td>\n",
       "      <td>249.72</td>\n",
       "      <td>250.0000</td>\n",
       "      <td>247.3700</td>\n",
       "      <td>249.28</td>\n",
       "      <td>6344766.0</td>\n",
       "      <td>243.644167</td>\n",
       "      <td>66.638084</td>\n",
       "      <td>36527257.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-01-21 05:00:00+00:00</td>\n",
       "      <td>247.68</td>\n",
       "      <td>249.2900</td>\n",
       "      <td>246.5703</td>\n",
       "      <td>247.94</td>\n",
       "      <td>5447925.0</td>\n",
       "      <td>244.313333</td>\n",
       "      <td>62.388291</td>\n",
       "      <td>31079332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-01-22 05:00:00+00:00</td>\n",
       "      <td>249.76</td>\n",
       "      <td>253.5900</td>\n",
       "      <td>249.0000</td>\n",
       "      <td>250.05</td>\n",
       "      <td>5980967.0</td>\n",
       "      <td>245.478333</td>\n",
       "      <td>66.058865</td>\n",
       "      <td>37060299.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-01-23 05:00:00+00:00</td>\n",
       "      <td>251.88</td>\n",
       "      <td>253.2000</td>\n",
       "      <td>248.1500</td>\n",
       "      <td>252.86</td>\n",
       "      <td>6113790.0</td>\n",
       "      <td>246.795000</td>\n",
       "      <td>70.226160</td>\n",
       "      <td>43174089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-01-24 05:00:00+00:00</td>\n",
       "      <td>257.87</td>\n",
       "      <td>259.5000</td>\n",
       "      <td>248.3000</td>\n",
       "      <td>250.48</td>\n",
       "      <td>9337839.0</td>\n",
       "      <td>247.674167</td>\n",
       "      <td>63.153531</td>\n",
       "      <td>33836250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-01-27 05:00:00+00:00</td>\n",
       "      <td>239.00</td>\n",
       "      <td>242.2500</td>\n",
       "      <td>232.2300</td>\n",
       "      <td>240.20</td>\n",
       "      <td>11763989.0</td>\n",
       "      <td>247.659167</td>\n",
       "      <td>43.006315</td>\n",
       "      <td>22072261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-01-28 05:00:00+00:00</td>\n",
       "      <td>242.85</td>\n",
       "      <td>249.2700</td>\n",
       "      <td>240.7143</td>\n",
       "      <td>247.97</td>\n",
       "      <td>7774401.0</td>\n",
       "      <td>248.071667</td>\n",
       "      <td>54.755243</td>\n",
       "      <td>29846662.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-01-29 05:00:00+00:00</td>\n",
       "      <td>247.28</td>\n",
       "      <td>248.8400</td>\n",
       "      <td>243.9400</td>\n",
       "      <td>245.54</td>\n",
       "      <td>6486280.0</td>\n",
       "      <td>248.173333</td>\n",
       "      <td>51.200443</td>\n",
       "      <td>23360382.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-01-30 05:00:00+00:00</td>\n",
       "      <td>241.67</td>\n",
       "      <td>246.3500</td>\n",
       "      <td>239.4000</td>\n",
       "      <td>245.81</td>\n",
       "      <td>7262590.0</td>\n",
       "      <td>247.659167</td>\n",
       "      <td>51.576615</td>\n",
       "      <td>30622972.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Date    open      high       low   Close      volume  \\\n",
       "0  2020-01-02 05:00:00+00:00  238.85  239.9100  236.7200  239.91   5941969.0   \n",
       "1  2020-01-03 05:00:00+00:00  235.00  237.8300  234.1000  236.07   5145208.0   \n",
       "2  2020-01-06 05:00:00+00:00  232.36  237.2700  231.2700  237.06   6572812.0   \n",
       "3  2020-01-07 05:00:00+00:00  238.20  241.7700  236.3900  239.93   7980144.0   \n",
       "4  2020-01-08 05:00:00+00:00  239.76  242.0400  238.1490  240.38   6931017.0   \n",
       "5  2020-01-09 05:00:00+00:00  243.55  245.9297  240.8550  243.02   6386053.0   \n",
       "6  2020-01-10 05:00:00+00:00  247.48  248.5500  243.7500  244.32   7911560.0   \n",
       "7  2020-01-13 05:00:00+00:00  247.66  252.9884  246.7500  251.98   8008451.0   \n",
       "8  2020-01-14 05:00:00+00:00  250.51  251.2800  246.7500  247.28   8989483.0   \n",
       "9  2020-01-15 05:00:00+00:00  247.79  248.6891  244.5300  245.57   6577646.0   \n",
       "10 2020-01-16 05:00:00+00:00  247.63  249.1700  246.7750  248.93   7104791.0   \n",
       "11 2020-01-17 05:00:00+00:00  249.72  250.0000  247.3700  249.28   6344766.0   \n",
       "12 2020-01-21 05:00:00+00:00  247.68  249.2900  246.5703  247.94   5447925.0   \n",
       "13 2020-01-22 05:00:00+00:00  249.76  253.5900  249.0000  250.05   5980967.0   \n",
       "14 2020-01-23 05:00:00+00:00  251.88  253.2000  248.1500  252.86   6113790.0   \n",
       "15 2020-01-24 05:00:00+00:00  257.87  259.5000  248.3000  250.48   9337839.0   \n",
       "16 2020-01-27 05:00:00+00:00  239.00  242.2500  232.2300  240.20  11763989.0   \n",
       "17 2020-01-28 05:00:00+00:00  242.85  249.2700  240.7143  247.97   7774401.0   \n",
       "18 2020-01-29 05:00:00+00:00  247.28  248.8400  243.9400  245.54   6486280.0   \n",
       "19 2020-01-30 05:00:00+00:00  241.67  246.3500  239.4000  245.81   7262590.0   \n",
       "\n",
       "           SMA        RSI         OBV  \n",
       "0     0.000000   0.000000         0.0  \n",
       "1     0.000000   0.000000  -5145208.0  \n",
       "2     0.000000  21.730950   1427604.0  \n",
       "3     0.000000  53.367920   9407748.0  \n",
       "4     0.000000  56.347335  16338765.0  \n",
       "5     0.000000  68.900960  22724818.0  \n",
       "6     0.000000  73.016122  30636378.0  \n",
       "7     0.000000  85.332239  38644829.0  \n",
       "8     0.000000  65.559779  29655346.0  \n",
       "9     0.000000  60.103112  23077700.0  \n",
       "10    0.000000  66.077643  30182491.0  \n",
       "11  243.644167  66.638084  36527257.0  \n",
       "12  244.313333  62.388291  31079332.0  \n",
       "13  245.478333  66.058865  37060299.0  \n",
       "14  246.795000  70.226160  43174089.0  \n",
       "15  247.674167  63.153531  33836250.0  \n",
       "16  247.659167  43.006315  22072261.0  \n",
       "17  248.071667  54.755243  29846662.0  \n",
       "18  248.173333  51.200443  23360382.0  \n",
       "19  247.659167  51.576615  30622972.0  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SMA']=TA.SMA(df,12)\n",
    "df[\"RSI\"]=TA.RSI(df)\n",
    "df['OBV']=TA.OBV(df)\n",
    "df.fillna(0,inplace=True)\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create a new environment\n",
    "\n",
    "def add_signals(env):\n",
    "    start = env.frame_bound[0]-env.window_size\n",
    "    end = env.frame_bound[1]\n",
    "    prices = env.df.loc[:,'low'].to_numpy()[start:end]\n",
    "    signal_features = env.df.loc[:,['low','volume','SMA','RSI','OBV']].to_numpy()[start:end]\n",
    "    return prices,signal_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class MycustomEnv(StocksEnv):\n",
    "    _process_data = add_signals\n",
    "\n",
    "env2 = MycustomEnv(df=df,window_size=12,frame_bound=(12,50))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Build environment and Train\n",
    "\n",
    "#env_maker = lambda: gym.make('stocks-v0',df=df,frame_bound=(5,250),window_size=5)\n",
    "env_maker = lambda: env2\n",
    "env = DummyVecEnv([env_maker])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using cpu device\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2059     |\n",
      "|    iterations         | 100      |\n",
      "|    time_elapsed       | 0        |\n",
      "|    total_timesteps    | 500      |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.495   |\n",
      "|    explained_variance | 0.365    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 99       |\n",
      "|    policy_loss        | 0.618    |\n",
      "|    value_loss         | 1.25     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2214     |\n",
      "|    iterations         | 200      |\n",
      "|    time_elapsed       | 0        |\n",
      "|    total_timesteps    | 1000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.69    |\n",
      "|    explained_variance | 0.117    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 199      |\n",
      "|    policy_loss        | -17.8    |\n",
      "|    value_loss         | 701      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2302     |\n",
      "|    iterations         | 300      |\n",
      "|    time_elapsed       | 0        |\n",
      "|    total_timesteps    | 1500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.575   |\n",
      "|    explained_variance | 0.743    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 299      |\n",
      "|    policy_loss        | 0.329    |\n",
      "|    value_loss         | 0.307    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2381     |\n",
      "|    iterations         | 400      |\n",
      "|    time_elapsed       | 0        |\n",
      "|    total_timesteps    | 2000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.627   |\n",
      "|    explained_variance | 0.419    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 399      |\n",
      "|    policy_loss        | -20.1    |\n",
      "|    value_loss         | 441      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2433     |\n",
      "|    iterations         | 500      |\n",
      "|    time_elapsed       | 1        |\n",
      "|    total_timesteps    | 2500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.615   |\n",
      "|    explained_variance | 0.0431   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 499      |\n",
      "|    policy_loss        | 5.5      |\n",
      "|    value_loss         | 92.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2455     |\n",
      "|    iterations         | 600      |\n",
      "|    time_elapsed       | 1        |\n",
      "|    total_timesteps    | 3000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.512   |\n",
      "|    explained_variance | -36.4    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 599      |\n",
      "|    policy_loss        | 0.153    |\n",
      "|    value_loss         | 0.499    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2489     |\n",
      "|    iterations         | 700      |\n",
      "|    time_elapsed       | 1        |\n",
      "|    total_timesteps    | 3500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.621   |\n",
      "|    explained_variance | -6.19    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 699      |\n",
      "|    policy_loss        | -0.134   |\n",
      "|    value_loss         | 0.121    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2521     |\n",
      "|    iterations         | 800      |\n",
      "|    time_elapsed       | 1        |\n",
      "|    total_timesteps    | 4000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.539   |\n",
      "|    explained_variance | 0.00427  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 799      |\n",
      "|    policy_loss        | -8.3     |\n",
      "|    value_loss         | 162      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2546     |\n",
      "|    iterations         | 900      |\n",
      "|    time_elapsed       | 1        |\n",
      "|    total_timesteps    | 4500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.615   |\n",
      "|    explained_variance | 0.0287   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 899      |\n",
      "|    policy_loss        | 3.77     |\n",
      "|    value_loss         | 79       |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2564      |\n",
      "|    iterations         | 1000      |\n",
      "|    time_elapsed       | 1         |\n",
      "|    total_timesteps    | 5000      |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.611    |\n",
      "|    explained_variance | -3.67e+12 |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 999       |\n",
      "|    policy_loss        | -2.47     |\n",
      "|    value_loss         | 28.7      |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2581     |\n",
      "|    iterations         | 1100     |\n",
      "|    time_elapsed       | 2        |\n",
      "|    total_timesteps    | 5500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.62    |\n",
      "|    explained_variance | 0.00348  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1099     |\n",
      "|    policy_loss        | 1.4      |\n",
      "|    value_loss         | 32.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2596     |\n",
      "|    iterations         | 1200     |\n",
      "|    time_elapsed       | 2        |\n",
      "|    total_timesteps    | 6000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.441   |\n",
      "|    explained_variance | 0.426    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1199     |\n",
      "|    policy_loss        | -2       |\n",
      "|    value_loss         | 21       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2608     |\n",
      "|    iterations         | 1300     |\n",
      "|    time_elapsed       | 2        |\n",
      "|    total_timesteps    | 6500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.489   |\n",
      "|    explained_variance | -2.62    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1299     |\n",
      "|    policy_loss        | 9.63     |\n",
      "|    value_loss         | 272      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2619     |\n",
      "|    iterations         | 1400     |\n",
      "|    time_elapsed       | 2        |\n",
      "|    total_timesteps    | 7000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.358   |\n",
      "|    explained_variance | 0.871    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1399     |\n",
      "|    policy_loss        | -0.0346  |\n",
      "|    value_loss         | 0.292    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2629     |\n",
      "|    iterations         | 1500     |\n",
      "|    time_elapsed       | 2        |\n",
      "|    total_timesteps    | 7500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.459   |\n",
      "|    explained_variance | 0.89     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1499     |\n",
      "|    policy_loss        | -0.0693  |\n",
      "|    value_loss         | 0.0991   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2638     |\n",
      "|    iterations         | 1600     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 8000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.416   |\n",
      "|    explained_variance | 0.723    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1599     |\n",
      "|    policy_loss        | -0.435   |\n",
      "|    value_loss         | 3.88     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2644     |\n",
      "|    iterations         | 1700     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 8500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.316   |\n",
      "|    explained_variance | 0.394    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1699     |\n",
      "|    policy_loss        | 0.0098   |\n",
      "|    value_loss         | 0.0101   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2651     |\n",
      "|    iterations         | 1800     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 9000     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.462   |\n",
      "|    explained_variance | 0.759    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1799     |\n",
      "|    policy_loss        | -0.886   |\n",
      "|    value_loss         | 3.1      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2657     |\n",
      "|    iterations         | 1900     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 9500     |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.349   |\n",
      "|    explained_variance | -1.53    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1899     |\n",
      "|    policy_loss        | -0.055   |\n",
      "|    value_loss         | 0.2      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2663     |\n",
      "|    iterations         | 2000     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 10000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.34    |\n",
      "|    explained_variance | 0.634    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 1999     |\n",
      "|    policy_loss        | 1.04     |\n",
      "|    value_loss         | 61.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2667     |\n",
      "|    iterations         | 2100     |\n",
      "|    time_elapsed       | 3        |\n",
      "|    total_timesteps    | 10500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.31    |\n",
      "|    explained_variance | -0.00341 |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2099     |\n",
      "|    policy_loss        | -4.23    |\n",
      "|    value_loss         | 91.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2670     |\n",
      "|    iterations         | 2200     |\n",
      "|    time_elapsed       | 4        |\n",
      "|    total_timesteps    | 11000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.25    |\n",
      "|    explained_variance | -0.134   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2199     |\n",
      "|    policy_loss        | -3.31    |\n",
      "|    value_loss         | 58.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2672     |\n",
      "|    iterations         | 2300     |\n",
      "|    time_elapsed       | 4        |\n",
      "|    total_timesteps    | 11500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.375   |\n",
      "|    explained_variance | 0.0918   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2299     |\n",
      "|    policy_loss        | 0.208    |\n",
      "|    value_loss         | 21.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2676     |\n",
      "|    iterations         | 2400     |\n",
      "|    time_elapsed       | 4        |\n",
      "|    total_timesteps    | 12000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.227   |\n",
      "|    explained_variance | 0.167    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2399     |\n",
      "|    policy_loss        | 0.311    |\n",
      "|    value_loss         | 32.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2680     |\n",
      "|    iterations         | 2500     |\n",
      "|    time_elapsed       | 4        |\n",
      "|    total_timesteps    | 12500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.381   |\n",
      "|    explained_variance | -6.82    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2499     |\n",
      "|    policy_loss        | -0.205   |\n",
      "|    value_loss         | 0.865    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2684     |\n",
      "|    iterations         | 2600     |\n",
      "|    time_elapsed       | 4        |\n",
      "|    total_timesteps    | 13000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.136   |\n",
      "|    explained_variance | 0.201    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2599     |\n",
      "|    policy_loss        | 0.17     |\n",
      "|    value_loss         | 22.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2687     |\n",
      "|    iterations         | 2700     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 13500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.327   |\n",
      "|    explained_variance | -1.51    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2699     |\n",
      "|    policy_loss        | 0.149    |\n",
      "|    value_loss         | 0.69     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2690     |\n",
      "|    iterations         | 2800     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 14000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0942  |\n",
      "|    explained_variance | -0.0424  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2799     |\n",
      "|    policy_loss        | -1.74    |\n",
      "|    value_loss         | 42.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2692     |\n",
      "|    iterations         | 2900     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 14500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.181   |\n",
      "|    explained_variance | 0.277    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2899     |\n",
      "|    policy_loss        | 0.0929   |\n",
      "|    value_loss         | 13       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2695     |\n",
      "|    iterations         | 3000     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 15000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.248   |\n",
      "|    explained_variance | -1.3     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 2999     |\n",
      "|    policy_loss        | 0.0897   |\n",
      "|    value_loss         | 0.601    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2696     |\n",
      "|    iterations         | 3100     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 15500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.132   |\n",
      "|    explained_variance | 0.256    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3099     |\n",
      "|    policy_loss        | 0.569    |\n",
      "|    value_loss         | 243      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2698     |\n",
      "|    iterations         | 3200     |\n",
      "|    time_elapsed       | 5        |\n",
      "|    total_timesteps    | 16000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.236   |\n",
      "|    explained_variance | -0.407   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3199     |\n",
      "|    policy_loss        | 0.141    |\n",
      "|    value_loss         | 3.68     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2700     |\n",
      "|    iterations         | 3300     |\n",
      "|    time_elapsed       | 6        |\n",
      "|    total_timesteps    | 16500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.177   |\n",
      "|    explained_variance | 0.507    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3299     |\n",
      "|    policy_loss        | 0.333    |\n",
      "|    value_loss         | 128      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2702     |\n",
      "|    iterations         | 3400     |\n",
      "|    time_elapsed       | 6        |\n",
      "|    total_timesteps    | 17000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.145   |\n",
      "|    explained_variance | 0.859    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3399     |\n",
      "|    policy_loss        | 0.0515   |\n",
      "|    value_loss         | 1.5      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2703     |\n",
      "|    iterations         | 3500     |\n",
      "|    time_elapsed       | 6        |\n",
      "|    total_timesteps    | 17500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.113   |\n",
      "|    explained_variance | 0.912    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3499     |\n",
      "|    policy_loss        | -2.89    |\n",
      "|    value_loss         | 15.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2704     |\n",
      "|    iterations         | 3600     |\n",
      "|    time_elapsed       | 6        |\n",
      "|    total_timesteps    | 18000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.263   |\n",
      "|    explained_variance | 0.73     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3599     |\n",
      "|    policy_loss        | 0.052    |\n",
      "|    value_loss         | 0.468    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2705     |\n",
      "|    iterations         | 3700     |\n",
      "|    time_elapsed       | 6        |\n",
      "|    total_timesteps    | 18500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.124   |\n",
      "|    explained_variance | 0.873    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3699     |\n",
      "|    policy_loss        | 0.000991 |\n",
      "|    value_loss         | 17.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2707     |\n",
      "|    iterations         | 3800     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 19000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.188   |\n",
      "|    explained_variance | 0.835    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3799     |\n",
      "|    policy_loss        | 0.0696   |\n",
      "|    value_loss         | 2.59     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2708     |\n",
      "|    iterations         | 3900     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 19500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.167   |\n",
      "|    explained_variance | 0.99     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3899     |\n",
      "|    policy_loss        | 0.00885  |\n",
      "|    value_loss         | 1.11     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2710     |\n",
      "|    iterations         | 4000     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 20000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.193   |\n",
      "|    explained_variance | 0.59     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 3999     |\n",
      "|    policy_loss        | 0.14     |\n",
      "|    value_loss         | 6.21     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2711     |\n",
      "|    iterations         | 4100     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 20500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.167   |\n",
      "|    explained_variance | 0.208    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4099     |\n",
      "|    policy_loss        | 0.0089   |\n",
      "|    value_loss         | 3.12     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2712     |\n",
      "|    iterations         | 4200     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 21000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.27    |\n",
      "|    explained_variance | -8.19    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4199     |\n",
      "|    policy_loss        | 0.0397   |\n",
      "|    value_loss         | 5.47     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2713     |\n",
      "|    iterations         | 4300     |\n",
      "|    time_elapsed       | 7        |\n",
      "|    total_timesteps    | 21500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.101   |\n",
      "|    explained_variance | -3.35    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4299     |\n",
      "|    policy_loss        | 0.305    |\n",
      "|    value_loss         | 13.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2713     |\n",
      "|    iterations         | 4400     |\n",
      "|    time_elapsed       | 8        |\n",
      "|    total_timesteps    | 22000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.221   |\n",
      "|    explained_variance | -13.1    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4399     |\n",
      "|    policy_loss        | 0.696    |\n",
      "|    value_loss         | 3.32     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2714     |\n",
      "|    iterations         | 4500     |\n",
      "|    time_elapsed       | 8        |\n",
      "|    total_timesteps    | 22500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.232   |\n",
      "|    explained_variance | 0.286    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4499     |\n",
      "|    policy_loss        | -3.09    |\n",
      "|    value_loss         | 64.7     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2715     |\n",
      "|    iterations         | 4600     |\n",
      "|    time_elapsed       | 8        |\n",
      "|    total_timesteps    | 23000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.157   |\n",
      "|    explained_variance | -2.26    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4599     |\n",
      "|    policy_loss        | 0.806    |\n",
      "|    value_loss         | 323      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2716     |\n",
      "|    iterations         | 4700     |\n",
      "|    time_elapsed       | 8        |\n",
      "|    total_timesteps    | 23500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.246   |\n",
      "|    explained_variance | 0.097    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4699     |\n",
      "|    policy_loss        | -0.575   |\n",
      "|    value_loss         | 19.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2717     |\n",
      "|    iterations         | 4800     |\n",
      "|    time_elapsed       | 8        |\n",
      "|    total_timesteps    | 24000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.12    |\n",
      "|    explained_variance | -0.0523  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4799     |\n",
      "|    policy_loss        | 0.585    |\n",
      "|    value_loss         | 249      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2718     |\n",
      "|    iterations         | 4900     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 24500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.226   |\n",
      "|    explained_variance | 0.963    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4899     |\n",
      "|    policy_loss        | -0.414   |\n",
      "|    value_loss         | 33.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2718     |\n",
      "|    iterations         | 5000     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 25000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0475  |\n",
      "|    explained_variance | 0.0871   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 4999     |\n",
      "|    policy_loss        | 0.221    |\n",
      "|    value_loss         | 678      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2718     |\n",
      "|    iterations         | 5100     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 25500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.369   |\n",
      "|    explained_variance | 0.976    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5099     |\n",
      "|    policy_loss        | -0.843   |\n",
      "|    value_loss         | 21.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2719     |\n",
      "|    iterations         | 5200     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 26000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0855  |\n",
      "|    explained_variance | 0.0851   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5199     |\n",
      "|    policy_loss        | 0.203    |\n",
      "|    value_loss         | 302      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2719     |\n",
      "|    iterations         | 5300     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 26500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.259   |\n",
      "|    explained_variance | 0.989    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5299     |\n",
      "|    policy_loss        | -0.649   |\n",
      "|    value_loss         | 6.55     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2719     |\n",
      "|    iterations         | 5400     |\n",
      "|    time_elapsed       | 9        |\n",
      "|    total_timesteps    | 27000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.166   |\n",
      "|    explained_variance | 0.255    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5399     |\n",
      "|    policy_loss        | -0.243   |\n",
      "|    value_loss         | 122      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2719     |\n",
      "|    iterations         | 5500     |\n",
      "|    time_elapsed       | 10       |\n",
      "|    total_timesteps    | 27500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.227   |\n",
      "|    explained_variance | 0.998    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5499     |\n",
      "|    policy_loss        | 0.0389   |\n",
      "|    value_loss         | 0.614    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2720     |\n",
      "|    iterations         | 5600     |\n",
      "|    time_elapsed       | 10       |\n",
      "|    total_timesteps    | 28000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.26    |\n",
      "|    explained_variance | 0.453    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5599     |\n",
      "|    policy_loss        | -1.55    |\n",
      "|    value_loss         | 207      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2721     |\n",
      "|    iterations         | 5700     |\n",
      "|    time_elapsed       | 10       |\n",
      "|    total_timesteps    | 28500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.051   |\n",
      "|    explained_variance | 0.671    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5699     |\n",
      "|    policy_loss        | -0.00316 |\n",
      "|    value_loss         | 0.656    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2722     |\n",
      "|    iterations         | 5800     |\n",
      "|    time_elapsed       | 10       |\n",
      "|    total_timesteps    | 29000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.106   |\n",
      "|    explained_variance | -0.538   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5799     |\n",
      "|    policy_loss        | -1.18    |\n",
      "|    value_loss         | 1.91e+03 |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2723     |\n",
      "|    iterations         | 5900     |\n",
      "|    time_elapsed       | 10       |\n",
      "|    total_timesteps    | 29500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0851  |\n",
      "|    explained_variance | 0.998    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5899     |\n",
      "|    policy_loss        | -0.00981 |\n",
      "|    value_loss         | 0.0884   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2724     |\n",
      "|    iterations         | 6000     |\n",
      "|    time_elapsed       | 11       |\n",
      "|    total_timesteps    | 30000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.377   |\n",
      "|    explained_variance | -0.889   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 5999     |\n",
      "|    policy_loss        | -19.9    |\n",
      "|    value_loss         | 1.8e+03  |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2724     |\n",
      "|    iterations         | 6100     |\n",
      "|    time_elapsed       | 11       |\n",
      "|    total_timesteps    | 30500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.233   |\n",
      "|    explained_variance | 0.919    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6099     |\n",
      "|    policy_loss        | 0.232    |\n",
      "|    value_loss         | 1.83     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2724     |\n",
      "|    iterations         | 6200     |\n",
      "|    time_elapsed       | 11       |\n",
      "|    total_timesteps    | 31000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.236   |\n",
      "|    explained_variance | -0.487   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6199     |\n",
      "|    policy_loss        | -4.37    |\n",
      "|    value_loss         | 1.36e+03 |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2725     |\n",
      "|    iterations         | 6300     |\n",
      "|    time_elapsed       | 11       |\n",
      "|    total_timesteps    | 31500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0228  |\n",
      "|    explained_variance | 0.99     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6299     |\n",
      "|    policy_loss        | 0.00654  |\n",
      "|    value_loss         | 0.993    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2726     |\n",
      "|    iterations         | 6400     |\n",
      "|    time_elapsed       | 11       |\n",
      "|    total_timesteps    | 32000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0739  |\n",
      "|    explained_variance | -0.187   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6399     |\n",
      "|    policy_loss        | -0.617   |\n",
      "|    value_loss         | 750      |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2726      |\n",
      "|    iterations         | 6500      |\n",
      "|    time_elapsed       | 11        |\n",
      "|    total_timesteps    | 32500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.000563 |\n",
      "|    explained_variance | 0.998     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 6499      |\n",
      "|    policy_loss        | 3.67e-05  |\n",
      "|    value_loss         | 0.381     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2727     |\n",
      "|    iterations         | 6600     |\n",
      "|    time_elapsed       | 12       |\n",
      "|    total_timesteps    | 33000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0187  |\n",
      "|    explained_variance | -28      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6599     |\n",
      "|    policy_loss        | 0.00296  |\n",
      "|    value_loss         | 7.41     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2728     |\n",
      "|    iterations         | 6700     |\n",
      "|    time_elapsed       | 12       |\n",
      "|    total_timesteps    | 33500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0791  |\n",
      "|    explained_variance | 0.784    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6699     |\n",
      "|    policy_loss        | 0.0187   |\n",
      "|    value_loss         | 0.185    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2728     |\n",
      "|    iterations         | 6800     |\n",
      "|    time_elapsed       | 12       |\n",
      "|    total_timesteps    | 34000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0281  |\n",
      "|    explained_variance | -12.2    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6799     |\n",
      "|    policy_loss        | -0.00679 |\n",
      "|    value_loss         | 5.99     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2729     |\n",
      "|    iterations         | 6900     |\n",
      "|    time_elapsed       | 12       |\n",
      "|    total_timesteps    | 34500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0669  |\n",
      "|    explained_variance | 0.102    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6899     |\n",
      "|    policy_loss        | 0.104    |\n",
      "|    value_loss         | 20       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2729     |\n",
      "|    iterations         | 7000     |\n",
      "|    time_elapsed       | 12       |\n",
      "|    total_timesteps    | 35000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0394  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 6999     |\n",
      "|    policy_loss        | -0.00287 |\n",
      "|    value_loss         | 0.111    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2730     |\n",
      "|    iterations         | 7100     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 35500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0641  |\n",
      "|    explained_variance | -0.751   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7099     |\n",
      "|    policy_loss        | 0.0435   |\n",
      "|    value_loss         | 4.91     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2731     |\n",
      "|    iterations         | 7200     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 36000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00891 |\n",
      "|    explained_variance | 0.991    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7199     |\n",
      "|    policy_loss        | 0.00185  |\n",
      "|    value_loss         | 1.59     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2731     |\n",
      "|    iterations         | 7300     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 36500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.21    |\n",
      "|    explained_variance | -1.84    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7299     |\n",
      "|    policy_loss        | 1.45     |\n",
      "|    value_loss         | 14.9     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 7400     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 37000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0202  |\n",
      "|    explained_variance | 0.998    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7399     |\n",
      "|    policy_loss        | -0.00066 |\n",
      "|    value_loss         | 0.536    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 7500     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 37500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.115   |\n",
      "|    explained_variance | -2.26    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7499     |\n",
      "|    policy_loss        | -2.17    |\n",
      "|    value_loss         | 18.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 7600     |\n",
      "|    time_elapsed       | 13       |\n",
      "|    total_timesteps    | 38000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.122   |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7599     |\n",
      "|    policy_loss        | -0.086   |\n",
      "|    value_loss         | 0.311    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2733     |\n",
      "|    iterations         | 7700     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 38500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.105   |\n",
      "|    explained_variance | -5.45    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7699     |\n",
      "|    policy_loss        | 0.249    |\n",
      "|    value_loss         | 104      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2733     |\n",
      "|    iterations         | 7800     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 39000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.13    |\n",
      "|    explained_variance | 0.976    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7799     |\n",
      "|    policy_loss        | -0.0252  |\n",
      "|    value_loss         | 0.108    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 7900     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 39500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.128   |\n",
      "|    explained_variance | -40.6    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7899     |\n",
      "|    policy_loss        | 0.115    |\n",
      "|    value_loss         | 60.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 8000     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 40000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.13    |\n",
      "|    explained_variance | -1.84    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 7999     |\n",
      "|    policy_loss        | -6.54    |\n",
      "|    value_loss         | 320      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 8100     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 40500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0693  |\n",
      "|    explained_variance | -14.1    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8099     |\n",
      "|    policy_loss        | 0.0643   |\n",
      "|    value_loss         | 37.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 8200     |\n",
      "|    time_elapsed       | 14       |\n",
      "|    total_timesteps    | 41000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.198   |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8199     |\n",
      "|    policy_loss        | -0.016   |\n",
      "|    value_loss         | 0.0891   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 8300     |\n",
      "|    time_elapsed       | 15       |\n",
      "|    total_timesteps    | 41500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0285  |\n",
      "|    explained_variance | -16.2    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8299     |\n",
      "|    policy_loss        | 0.0865   |\n",
      "|    value_loss         | 172      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2731     |\n",
      "|    iterations         | 8400     |\n",
      "|    time_elapsed       | 15       |\n",
      "|    total_timesteps    | 42000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0858  |\n",
      "|    explained_variance | 0.983    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8399     |\n",
      "|    policy_loss        | 0.00244  |\n",
      "|    value_loss         | 0.281    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2731     |\n",
      "|    iterations         | 8500     |\n",
      "|    time_elapsed       | 15       |\n",
      "|    total_timesteps    | 42500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.032   |\n",
      "|    explained_variance | 0.178    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8499     |\n",
      "|    policy_loss        | 0.0652   |\n",
      "|    value_loss         | 117      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 8600     |\n",
      "|    time_elapsed       | 15       |\n",
      "|    total_timesteps    | 43000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0762  |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8599     |\n",
      "|    policy_loss        | -1.67    |\n",
      "|    value_loss         | 13.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 8700     |\n",
      "|    time_elapsed       | 15       |\n",
      "|    total_timesteps    | 43500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.024   |\n",
      "|    explained_variance | 0.782    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8699     |\n",
      "|    policy_loss        | 0.0677   |\n",
      "|    value_loss         | 351      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 8800     |\n",
      "|    time_elapsed       | 16       |\n",
      "|    total_timesteps    | 44000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.175   |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8799     |\n",
      "|    policy_loss        | 0.0283   |\n",
      "|    value_loss         | 0.22     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 8900     |\n",
      "|    time_elapsed       | 16       |\n",
      "|    total_timesteps    | 44500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0661  |\n",
      "|    explained_variance | 0.537    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8899     |\n",
      "|    policy_loss        | 0.0162   |\n",
      "|    value_loss         | 62.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2732     |\n",
      "|    iterations         | 9000     |\n",
      "|    time_elapsed       | 16       |\n",
      "|    total_timesteps    | 45000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0984  |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 8999     |\n",
      "|    policy_loss        | -0.00262 |\n",
      "|    value_loss         | 0.0841   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2733     |\n",
      "|    iterations         | 9100     |\n",
      "|    time_elapsed       | 16       |\n",
      "|    total_timesteps    | 45500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.17    |\n",
      "|    explained_variance | 0.607    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9099     |\n",
      "|    policy_loss        | -1.08    |\n",
      "|    value_loss         | 156      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2733     |\n",
      "|    iterations         | 9200     |\n",
      "|    time_elapsed       | 16       |\n",
      "|    total_timesteps    | 46000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.168   |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9199     |\n",
      "|    policy_loss        | 0.00801  |\n",
      "|    value_loss         | 0.0496   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 9300     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 46500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.14    |\n",
      "|    explained_variance | -0.0404  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9299     |\n",
      "|    policy_loss        | -1.86    |\n",
      "|    value_loss         | 857      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 9400     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 47000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0906  |\n",
      "|    explained_variance | 0.989    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9399     |\n",
      "|    policy_loss        | -0.00354 |\n",
      "|    value_loss         | 0.0495   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 9500     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 47500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.048   |\n",
      "|    explained_variance | -3.64    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9499     |\n",
      "|    policy_loss        | -0.614   |\n",
      "|    value_loss         | 3.51e+03 |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 9600     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 48000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0904  |\n",
      "|    explained_variance | 0.991    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9599     |\n",
      "|    policy_loss        | -0.0025  |\n",
      "|    value_loss         | 0.154    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 9700     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 48500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00913 |\n",
      "|    explained_variance | -2.08    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9699     |\n",
      "|    policy_loss        | -0.0801  |\n",
      "|    value_loss         | 2.8e+03  |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2734     |\n",
      "|    iterations         | 9800     |\n",
      "|    time_elapsed       | 17       |\n",
      "|    total_timesteps    | 49000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.011   |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9799     |\n",
      "|    policy_loss        | 0.000684 |\n",
      "|    value_loss         | 0.12     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 9900     |\n",
      "|    time_elapsed       | 18       |\n",
      "|    total_timesteps    | 49500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0413  |\n",
      "|    explained_variance | -0.989   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9899     |\n",
      "|    policy_loss        | -0.205   |\n",
      "|    value_loss         | 1.62e+03 |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 10000    |\n",
      "|    time_elapsed       | 18       |\n",
      "|    total_timesteps    | 50000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00629 |\n",
      "|    explained_variance | 0.994    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 9999     |\n",
      "|    policy_loss        | 0.000231 |\n",
      "|    value_loss         | 0.151    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2735     |\n",
      "|    iterations         | 10100    |\n",
      "|    time_elapsed       | 18       |\n",
      "|    total_timesteps    | 50500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0502  |\n",
      "|    explained_variance | -0.296   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10099    |\n",
      "|    policy_loss        | -0.0603  |\n",
      "|    value_loss         | 741      |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2735      |\n",
      "|    iterations         | 10200     |\n",
      "|    time_elapsed       | 18        |\n",
      "|    total_timesteps    | 51000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.0176   |\n",
      "|    explained_variance | 0.986     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 10199     |\n",
      "|    policy_loss        | -0.000695 |\n",
      "|    value_loss         | 0.103     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2736     |\n",
      "|    iterations         | 10300    |\n",
      "|    time_elapsed       | 18       |\n",
      "|    total_timesteps    | 51500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.024   |\n",
      "|    explained_variance | -22.7    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10299    |\n",
      "|    policy_loss        | 0.00285  |\n",
      "|    value_loss         | 8.51     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2736     |\n",
      "|    iterations         | 10400    |\n",
      "|    time_elapsed       | 19       |\n",
      "|    total_timesteps    | 52000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0413  |\n",
      "|    explained_variance | 0.668    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10399    |\n",
      "|    policy_loss        | 0.0145   |\n",
      "|    value_loss         | 1.47     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2736     |\n",
      "|    iterations         | 10500    |\n",
      "|    time_elapsed       | 19       |\n",
      "|    total_timesteps    | 52500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0178  |\n",
      "|    explained_variance | -0.486   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10499    |\n",
      "|    policy_loss        | 0.000477 |\n",
      "|    value_loss         | 0.352    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2736     |\n",
      "|    iterations         | 10600    |\n",
      "|    time_elapsed       | 19       |\n",
      "|    total_timesteps    | 53000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0224  |\n",
      "|    explained_variance | 0.989    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10599    |\n",
      "|    policy_loss        | 0.0101   |\n",
      "|    value_loss         | 5.94     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2736     |\n",
      "|    iterations         | 10700    |\n",
      "|    time_elapsed       | 19       |\n",
      "|    total_timesteps    | 53500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0522  |\n",
      "|    explained_variance | 0.996    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10699    |\n",
      "|    policy_loss        | -0.0195  |\n",
      "|    value_loss         | 1.19     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2737      |\n",
      "|    iterations         | 10800     |\n",
      "|    time_elapsed       | 19        |\n",
      "|    total_timesteps    | 54000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.0427   |\n",
      "|    explained_variance | 0.896     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 10799     |\n",
      "|    policy_loss        | -0.000129 |\n",
      "|    value_loss         | 0.107     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2737     |\n",
      "|    iterations         | 10900    |\n",
      "|    time_elapsed       | 19       |\n",
      "|    total_timesteps    | 54500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0255  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10899    |\n",
      "|    policy_loss        | -0.00857 |\n",
      "|    value_loss         | 2.64     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2737     |\n",
      "|    iterations         | 11000    |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 55000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0993  |\n",
      "|    explained_variance | 0.935    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 10999    |\n",
      "|    policy_loss        | 0.0519   |\n",
      "|    value_loss         | 2.85     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2737      |\n",
      "|    iterations         | 11100     |\n",
      "|    time_elapsed       | 20        |\n",
      "|    total_timesteps    | 55500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.0224   |\n",
      "|    explained_variance | 1         |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 11099     |\n",
      "|    policy_loss        | -6.24e-07 |\n",
      "|    value_loss         | 0.075     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2737     |\n",
      "|    iterations         | 11200    |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 56000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.045   |\n",
      "|    explained_variance | 0.155    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11199    |\n",
      "|    policy_loss        | -0.0309  |\n",
      "|    value_loss         | 13.3     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2738     |\n",
      "|    iterations         | 11300    |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 56500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.147   |\n",
      "|    explained_variance | 0.995    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11299    |\n",
      "|    policy_loss        | -0.245   |\n",
      "|    value_loss         | 2.11     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2738     |\n",
      "|    iterations         | 11400    |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 57000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.129   |\n",
      "|    explained_variance | -5.12    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11399    |\n",
      "|    policy_loss        | 0.506    |\n",
      "|    value_loss         | 75       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2738     |\n",
      "|    iterations         | 11500    |\n",
      "|    time_elapsed       | 20       |\n",
      "|    total_timesteps    | 57500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0228  |\n",
      "|    explained_variance | 0.992    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11499    |\n",
      "|    policy_loss        | -0.00109 |\n",
      "|    value_loss         | 0.0612   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2739     |\n",
      "|    iterations         | 11600    |\n",
      "|    time_elapsed       | 21       |\n",
      "|    total_timesteps    | 58000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0678  |\n",
      "|    explained_variance | -50.4    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11599    |\n",
      "|    policy_loss        | -0.0461  |\n",
      "|    value_loss         | 25.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2739     |\n",
      "|    iterations         | 11700    |\n",
      "|    time_elapsed       | 21       |\n",
      "|    total_timesteps    | 58500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.085   |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11699    |\n",
      "|    policy_loss        | -0.0012  |\n",
      "|    value_loss         | 0.0163   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2739     |\n",
      "|    iterations         | 11800    |\n",
      "|    time_elapsed       | 21       |\n",
      "|    total_timesteps    | 59000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.22    |\n",
      "|    explained_variance | -141     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11799    |\n",
      "|    policy_loss        | -5.69    |\n",
      "|    value_loss         | 83.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 11900    |\n",
      "|    time_elapsed       | 21       |\n",
      "|    total_timesteps    | 59500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0489  |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11899    |\n",
      "|    policy_loss        | -0.00388 |\n",
      "|    value_loss         | 0.13     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12000    |\n",
      "|    time_elapsed       | 21       |\n",
      "|    total_timesteps    | 60000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.169   |\n",
      "|    explained_variance | -6.41    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 11999    |\n",
      "|    policy_loss        | -0.67    |\n",
      "|    value_loss         | 24.4     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12100    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 60500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0329  |\n",
      "|    explained_variance | 0.972    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12099    |\n",
      "|    policy_loss        | 0.0073   |\n",
      "|    value_loss         | 4.02     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12200    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 61000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0517  |\n",
      "|    explained_variance | 0.737    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12199    |\n",
      "|    policy_loss        | 0.0106   |\n",
      "|    value_loss         | 70.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12300    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 61500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0604  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12299    |\n",
      "|    policy_loss        | 0.0109   |\n",
      "|    value_loss         | 1.12     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12400    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 62000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0745  |\n",
      "|    explained_variance | 0.892    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12399    |\n",
      "|    policy_loss        | 0.648    |\n",
      "|    value_loss         | 454      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 12500    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 62500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.113   |\n",
      "|    explained_variance | 0.998    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12499    |\n",
      "|    policy_loss        | 0.207    |\n",
      "|    value_loss         | 5.66     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 12600    |\n",
      "|    time_elapsed       | 22       |\n",
      "|    total_timesteps    | 63000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.091   |\n",
      "|    explained_variance | 0.43     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12599    |\n",
      "|    policy_loss        | -0.557   |\n",
      "|    value_loss         | 81.2     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 12700    |\n",
      "|    time_elapsed       | 23       |\n",
      "|    total_timesteps    | 63500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.132   |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12699    |\n",
      "|    policy_loss        | -0.0328  |\n",
      "|    value_loss         | 0.174    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 12800    |\n",
      "|    time_elapsed       | 23       |\n",
      "|    total_timesteps    | 64000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0272  |\n",
      "|    explained_variance | 0.54     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12799    |\n",
      "|    policy_loss        | -0.0455  |\n",
      "|    value_loss         | 144      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 12900    |\n",
      "|    time_elapsed       | 23       |\n",
      "|    total_timesteps    | 64500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0609  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12899    |\n",
      "|    policy_loss        | -0.0231  |\n",
      "|    value_loss         | 0.825    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13000    |\n",
      "|    time_elapsed       | 23       |\n",
      "|    total_timesteps    | 65000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0508  |\n",
      "|    explained_variance | -0.563   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 12999    |\n",
      "|    policy_loss        | -0.224   |\n",
      "|    value_loss         | 397      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13100    |\n",
      "|    time_elapsed       | 23       |\n",
      "|    total_timesteps    | 65500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0931  |\n",
      "|    explained_variance | 0.941    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13099    |\n",
      "|    policy_loss        | 0.0156   |\n",
      "|    value_loss         | 0.69     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13200    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 66000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0159  |\n",
      "|    explained_variance | -2.54    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13199    |\n",
      "|    policy_loss        | -0.0963  |\n",
      "|    value_loss         | 1.24e+03 |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13300    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 66500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0192  |\n",
      "|    explained_variance | 0.965    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13299    |\n",
      "|    policy_loss        | 0.00507  |\n",
      "|    value_loss         | 1.84     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 13400    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 67000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.175   |\n",
      "|    explained_variance | -0.245   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13399    |\n",
      "|    policy_loss        | -3.7     |\n",
      "|    value_loss         | 977      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 13500    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 67500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0964  |\n",
      "|    explained_variance | 0.987    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13499    |\n",
      "|    policy_loss        | 0.00352  |\n",
      "|    value_loss         | 0.186    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 13600    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 68000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.186   |\n",
      "|    explained_variance | -142     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13599    |\n",
      "|    policy_loss        | 0.323    |\n",
      "|    value_loss         | 36.8     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13700    |\n",
      "|    time_elapsed       | 24       |\n",
      "|    total_timesteps    | 68500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0101  |\n",
      "|    explained_variance | 0.932    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13699    |\n",
      "|    policy_loss        | 0.00114  |\n",
      "|    value_loss         | 1.56     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 13800    |\n",
      "|    time_elapsed       | 25       |\n",
      "|    total_timesteps    | 69000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.044   |\n",
      "|    explained_variance | -30.3    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13799    |\n",
      "|    policy_loss        | -0.0509  |\n",
      "|    value_loss         | 8.94     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 13900    |\n",
      "|    time_elapsed       | 25       |\n",
      "|    total_timesteps    | 69500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00155 |\n",
      "|    explained_variance | 0.988    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13899    |\n",
      "|    policy_loss        | 3.7e-05  |\n",
      "|    value_loss         | 0.189    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 14000    |\n",
      "|    time_elapsed       | 25       |\n",
      "|    total_timesteps    | 70000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0317  |\n",
      "|    explained_variance | -0.782   |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 13999    |\n",
      "|    policy_loss        | 0.00429  |\n",
      "|    value_loss         | 1.04     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 14100    |\n",
      "|    time_elapsed       | 25       |\n",
      "|    total_timesteps    | 70500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.012   |\n",
      "|    explained_variance | 0.958    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14099    |\n",
      "|    policy_loss        | 0.000771 |\n",
      "|    value_loss         | 0.0805   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 14200    |\n",
      "|    time_elapsed       | 25       |\n",
      "|    total_timesteps    | 71000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0821  |\n",
      "|    explained_variance | -3.02    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14199    |\n",
      "|    policy_loss        | 0.0146   |\n",
      "|    value_loss         | 1.97     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 14300    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 71500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0174  |\n",
      "|    explained_variance | 0.786    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14299    |\n",
      "|    policy_loss        | 0.00429  |\n",
      "|    value_loss         | 0.519    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 14400    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 72000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0212  |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14399    |\n",
      "|    policy_loss        | -0.00195 |\n",
      "|    value_loss         | 0.203    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2740     |\n",
      "|    iterations         | 14500    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 72500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00687 |\n",
      "|    explained_variance | 0.96     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14499    |\n",
      "|    policy_loss        | 0.000343 |\n",
      "|    value_loss         | 0.078    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 14600    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 73000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00363 |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14599    |\n",
      "|    policy_loss        | 0.00023  |\n",
      "|    value_loss         | 0.446    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 14700    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 73500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0153  |\n",
      "|    explained_variance | 0.991    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14699    |\n",
      "|    policy_loss        | 0.000617 |\n",
      "|    value_loss         | 0.0375   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 14800    |\n",
      "|    time_elapsed       | 26       |\n",
      "|    total_timesteps    | 74000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.149   |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14799    |\n",
      "|    policy_loss        | -0.0184  |\n",
      "|    value_loss         | 0.292    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 14900    |\n",
      "|    time_elapsed       | 27       |\n",
      "|    total_timesteps    | 74500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0193  |\n",
      "|    explained_variance | 0.814    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14899    |\n",
      "|    policy_loss        | -0.0133  |\n",
      "|    value_loss         | 19.1     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 15000    |\n",
      "|    time_elapsed       | 27       |\n",
      "|    total_timesteps    | 75000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0416  |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 14999    |\n",
      "|    policy_loss        | 0.00389  |\n",
      "|    value_loss         | 0.351    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 15100    |\n",
      "|    time_elapsed       | 27       |\n",
      "|    total_timesteps    | 75500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.175   |\n",
      "|    explained_variance | 0.963    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15099    |\n",
      "|    policy_loss        | -0.00251 |\n",
      "|    value_loss         | 0.0755   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 15200    |\n",
      "|    time_elapsed       | 27       |\n",
      "|    total_timesteps    | 76000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0366  |\n",
      "|    explained_variance | 0.956    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15199    |\n",
      "|    policy_loss        | -0.00469 |\n",
      "|    value_loss         | 0.371    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2741     |\n",
      "|    iterations         | 15300    |\n",
      "|    time_elapsed       | 27       |\n",
      "|    total_timesteps    | 76500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0484  |\n",
      "|    explained_variance | 0.963    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15299    |\n",
      "|    policy_loss        | 0.00149  |\n",
      "|    value_loss         | 0.0818   |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2741      |\n",
      "|    iterations         | 15400     |\n",
      "|    time_elapsed       | 28        |\n",
      "|    total_timesteps    | 77000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.0137   |\n",
      "|    explained_variance | 0.995     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 15399     |\n",
      "|    policy_loss        | -0.000131 |\n",
      "|    value_loss         | 0.0923    |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2742     |\n",
      "|    iterations         | 15500    |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 77500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.118   |\n",
      "|    explained_variance | 0.99     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15499    |\n",
      "|    policy_loss        | -0.0475  |\n",
      "|    value_loss         | 0.46     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2742     |\n",
      "|    iterations         | 15600    |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 78000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.113   |\n",
      "|    explained_variance | -1.13    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15599    |\n",
      "|    policy_loss        | -4.85    |\n",
      "|    value_loss         | 173      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2742     |\n",
      "|    iterations         | 15700    |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 78500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0403  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15699    |\n",
      "|    policy_loss        | 0.0232   |\n",
      "|    value_loss         | 6.66     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2742     |\n",
      "|    iterations         | 15800    |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 79000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0127  |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15799    |\n",
      "|    policy_loss        | 0.000371 |\n",
      "|    value_loss         | 0.0558   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2742     |\n",
      "|    iterations         | 15900    |\n",
      "|    time_elapsed       | 28       |\n",
      "|    total_timesteps    | 79500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0527  |\n",
      "|    explained_variance | 0.646    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15899    |\n",
      "|    policy_loss        | 0.0491   |\n",
      "|    value_loss         | 100      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16000    |\n",
      "|    time_elapsed       | 29       |\n",
      "|    total_timesteps    | 80000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0132  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 15999    |\n",
      "|    policy_loss        | 0.000181 |\n",
      "|    value_loss         | 0.538    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16100    |\n",
      "|    time_elapsed       | 29       |\n",
      "|    total_timesteps    | 80500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.134   |\n",
      "|    explained_variance | 0.849    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16099    |\n",
      "|    policy_loss        | 3.05     |\n",
      "|    value_loss         | 623      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16200    |\n",
      "|    time_elapsed       | 29       |\n",
      "|    total_timesteps    | 81000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.13    |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16199    |\n",
      "|    policy_loss        | -0.0131  |\n",
      "|    value_loss         | 0.57     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16300    |\n",
      "|    time_elapsed       | 29       |\n",
      "|    total_timesteps    | 81500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0222  |\n",
      "|    explained_variance | 0.877    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16299    |\n",
      "|    policy_loss        | -0.00688 |\n",
      "|    value_loss         | 24.6     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16400    |\n",
      "|    time_elapsed       | 29       |\n",
      "|    total_timesteps    | 82000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0254  |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16399    |\n",
      "|    policy_loss        | 0.000852 |\n",
      "|    value_loss         | 0.0375   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16500    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 82500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0128  |\n",
      "|    explained_variance | 0.963    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16499    |\n",
      "|    policy_loss        | -0.00115 |\n",
      "|    value_loss         | 5.32     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16600    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 83000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.147   |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16599    |\n",
      "|    policy_loss        | 0.0286   |\n",
      "|    value_loss         | 0.133    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16700    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 83500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.101   |\n",
      "|    explained_variance | 0.887    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16699    |\n",
      "|    policy_loss        | -0.189   |\n",
      "|    value_loss         | 7.31     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16800    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 84000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0179  |\n",
      "|    explained_variance | 0.956    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16799    |\n",
      "|    policy_loss        | 0.000268 |\n",
      "|    value_loss         | 0.0504   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 16900    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 84500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.154   |\n",
      "|    explained_variance | 0.504    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16899    |\n",
      "|    policy_loss        | -0.238   |\n",
      "|    value_loss         | 22.5     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 17000    |\n",
      "|    time_elapsed       | 30       |\n",
      "|    total_timesteps    | 85000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.116   |\n",
      "|    explained_variance | 0.992    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 16999    |\n",
      "|    policy_loss        | -0.0265  |\n",
      "|    value_loss         | 0.388    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 17100    |\n",
      "|    time_elapsed       | 31       |\n",
      "|    total_timesteps    | 85500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0831  |\n",
      "|    explained_variance | -12.5    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17099    |\n",
      "|    policy_loss        | -0.139   |\n",
      "|    value_loss         | 4.37     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 17200    |\n",
      "|    time_elapsed       | 31       |\n",
      "|    total_timesteps    | 86000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00721 |\n",
      "|    explained_variance | 0.998    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17199    |\n",
      "|    policy_loss        | 0.000277 |\n",
      "|    value_loss         | 0.0511   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 17300    |\n",
      "|    time_elapsed       | 31       |\n",
      "|    total_timesteps    | 86500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0479  |\n",
      "|    explained_variance | 0.314    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17299    |\n",
      "|    policy_loss        | -0.0129  |\n",
      "|    value_loss         | 5.83     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2743      |\n",
      "|    iterations         | 17400     |\n",
      "|    time_elapsed       | 31        |\n",
      "|    total_timesteps    | 87000     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00275  |\n",
      "|    explained_variance | 0.996     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 17399     |\n",
      "|    policy_loss        | -0.000147 |\n",
      "|    value_loss         | 0.123     |\n",
      "-------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2744      |\n",
      "|    iterations         | 17500     |\n",
      "|    time_elapsed       | 31        |\n",
      "|    total_timesteps    | 87500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00232  |\n",
      "|    explained_variance | 0.612     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 17499     |\n",
      "|    policy_loss        | -0.000105 |\n",
      "|    value_loss         | 0.307     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2743     |\n",
      "|    iterations         | 17600    |\n",
      "|    time_elapsed       | 32       |\n",
      "|    total_timesteps    | 88000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00447 |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17599    |\n",
      "|    policy_loss        | 5.04e-05 |\n",
      "|    value_loss         | 0.0139   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 17700    |\n",
      "|    time_elapsed       | 32       |\n",
      "|    total_timesteps    | 88500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00746 |\n",
      "|    explained_variance | -0.0409  |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17699    |\n",
      "|    policy_loss        | -0.00171 |\n",
      "|    value_loss         | 0.592    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 17800    |\n",
      "|    time_elapsed       | 32       |\n",
      "|    total_timesteps    | 89000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0164  |\n",
      "|    explained_variance | 0.978    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17799    |\n",
      "|    policy_loss        | 0.000353 |\n",
      "|    value_loss         | 0.0634   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 17900    |\n",
      "|    time_elapsed       | 32       |\n",
      "|    total_timesteps    | 89500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.012   |\n",
      "|    explained_variance | 0.505    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17899    |\n",
      "|    policy_loss        | 0.000925 |\n",
      "|    value_loss         | 0.433    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 18000    |\n",
      "|    time_elapsed       | 32       |\n",
      "|    total_timesteps    | 90000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0732  |\n",
      "|    explained_variance | 0.99     |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 17999    |\n",
      "|    policy_loss        | 0.00584  |\n",
      "|    value_loss         | 0.0649   |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2744      |\n",
      "|    iterations         | 18100     |\n",
      "|    time_elapsed       | 32        |\n",
      "|    total_timesteps    | 90500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00946  |\n",
      "|    explained_variance | 0.999     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 18099     |\n",
      "|    policy_loss        | -0.000188 |\n",
      "|    value_loss         | 0.0749    |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2744     |\n",
      "|    iterations         | 18200    |\n",
      "|    time_elapsed       | 33       |\n",
      "|    total_timesteps    | 91000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.071   |\n",
      "|    explained_variance | 0.957    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18199    |\n",
      "|    policy_loss        | 0.00526  |\n",
      "|    value_loss         | 0.0753   |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2744      |\n",
      "|    iterations         | 18300     |\n",
      "|    time_elapsed       | 33        |\n",
      "|    total_timesteps    | 91500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00388  |\n",
      "|    explained_variance | 0.999     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 18299     |\n",
      "|    policy_loss        | -0.000292 |\n",
      "|    value_loss         | 0.989     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 18400    |\n",
      "|    time_elapsed       | 33       |\n",
      "|    total_timesteps    | 92000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00293 |\n",
      "|    explained_variance | 0.848    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18399    |\n",
      "|    policy_loss        | 0.000375 |\n",
      "|    value_loss         | 1.62     |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2745      |\n",
      "|    iterations         | 18500     |\n",
      "|    time_elapsed       | 33        |\n",
      "|    total_timesteps    | 92500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00456  |\n",
      "|    explained_variance | 0.999     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 18499     |\n",
      "|    policy_loss        | -0.000627 |\n",
      "|    value_loss         | 0.328     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 18600    |\n",
      "|    time_elapsed       | 33       |\n",
      "|    total_timesteps    | 93000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0128  |\n",
      "|    explained_variance | 0.923    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18599    |\n",
      "|    policy_loss        | 0.00052  |\n",
      "|    value_loss         | 0.151    |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 18700    |\n",
      "|    time_elapsed       | 34       |\n",
      "|    total_timesteps    | 93500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00849 |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18699    |\n",
      "|    policy_loss        | 8.77e-05 |\n",
      "|    value_loss         | 0.0493   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 18800    |\n",
      "|    time_elapsed       | 34       |\n",
      "|    total_timesteps    | 94000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00304 |\n",
      "|    explained_variance | 0.868    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18799    |\n",
      "|    policy_loss        | 2.73e-06 |\n",
      "|    value_loss         | 0.317    |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2745      |\n",
      "|    iterations         | 18900     |\n",
      "|    time_elapsed       | 34        |\n",
      "|    total_timesteps    | 94500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.0198   |\n",
      "|    explained_variance | 0.984     |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 18899     |\n",
      "|    policy_loss        | -0.000659 |\n",
      "|    value_loss         | 0.115     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19000    |\n",
      "|    time_elapsed       | 34       |\n",
      "|    total_timesteps    | 95000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.081   |\n",
      "|    explained_variance | -5.06    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 18999    |\n",
      "|    policy_loss        | -0.63    |\n",
      "|    value_loss         | 311      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19100    |\n",
      "|    time_elapsed       | 34       |\n",
      "|    total_timesteps    | 95500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.223   |\n",
      "|    explained_variance | -1.47    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19099    |\n",
      "|    policy_loss        | -3.93    |\n",
      "|    value_loss         | 186      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19200    |\n",
      "|    time_elapsed       | 34       |\n",
      "|    total_timesteps    | 96000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0573  |\n",
      "|    explained_variance | -58.3    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19199    |\n",
      "|    policy_loss        | -0.266   |\n",
      "|    value_loss         | 72       |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19300    |\n",
      "|    time_elapsed       | 35       |\n",
      "|    total_timesteps    | 96500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0402  |\n",
      "|    explained_variance | 0.9      |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19299    |\n",
      "|    policy_loss        | 0.0212   |\n",
      "|    value_loss         | 2.8      |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19400    |\n",
      "|    time_elapsed       | 35       |\n",
      "|    total_timesteps    | 97000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0199  |\n",
      "|    explained_variance | 0.948    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19399    |\n",
      "|    policy_loss        | 0.0143   |\n",
      "|    value_loss         | 12       |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2745      |\n",
      "|    iterations         | 19500     |\n",
      "|    time_elapsed       | 35        |\n",
      "|    total_timesteps    | 97500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00341  |\n",
      "|    explained_variance | 1         |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 19499     |\n",
      "|    policy_loss        | -5.84e-05 |\n",
      "|    value_loss         | 0.038     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19600    |\n",
      "|    time_elapsed       | 35       |\n",
      "|    total_timesteps    | 98000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.013   |\n",
      "|    explained_variance | 0.997    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19599    |\n",
      "|    policy_loss        | -0.00444 |\n",
      "|    value_loss         | 1.96     |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2745     |\n",
      "|    iterations         | 19700    |\n",
      "|    time_elapsed       | 35       |\n",
      "|    total_timesteps    | 98500    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.00359 |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19699    |\n",
      "|    policy_loss        | -1.2e-05 |\n",
      "|    value_loss         | 0.0196   |\n",
      "------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2746     |\n",
      "|    iterations         | 19800    |\n",
      "|    time_elapsed       | 36       |\n",
      "|    total_timesteps    | 99000    |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0159  |\n",
      "|    explained_variance | 0.999    |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19799    |\n",
      "|    policy_loss        | 0.00019  |\n",
      "|    value_loss         | 0.197    |\n",
      "------------------------------------\n",
      "-------------------------------------\n",
      "| time/                 |           |\n",
      "|    fps                | 2746      |\n",
      "|    iterations         | 19900     |\n",
      "|    time_elapsed       | 36        |\n",
      "|    total_timesteps    | 99500     |\n",
      "| train/                |           |\n",
      "|    entropy_loss       | -0.00602  |\n",
      "|    explained_variance | 1         |\n",
      "|    learning_rate      | 0.0007    |\n",
      "|    n_updates          | 19899     |\n",
      "|    policy_loss        | -8.22e-05 |\n",
      "|    value_loss         | 0.122     |\n",
      "-------------------------------------\n",
      "------------------------------------\n",
      "| time/                 |          |\n",
      "|    fps                | 2746     |\n",
      "|    iterations         | 20000    |\n",
      "|    time_elapsed       | 36       |\n",
      "|    total_timesteps    | 100000   |\n",
      "| train/                |          |\n",
      "|    entropy_loss       | -0.0697  |\n",
      "|    explained_variance | 1        |\n",
      "|    learning_rate      | 0.0007   |\n",
      "|    n_updates          | 19999    |\n",
      "|    policy_loss        | 0.0012   |\n",
      "|    value_loss         | 0.048    |\n",
      "------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<stable_baselines3.a2c.a2c.A2C at 0x1502ae700>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = A2C('MlpPolicy',env,verbose=1)\n",
    "\n",
    "model.learn(total_timesteps=100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "info: {'total_reward': 39.79860000000002, 'total_profit': 0.9810571569068982, 'position': 0}\n"
     ]
    }
   ],
   "source": [
    "#Evaluation\n",
    "\n",
    "env = MycustomEnv(df=df,window_size=12,frame_bound=(80,120))\n",
    "#env = gym.make('stocks-v0',df=df,frame_bound=(5,250),window_size=5)\n",
    "observation = env.reset()\n",
    "while True:\n",
    "    action = env.action_space.sample()\n",
    "    observation, reward, done, info = env.step(action)\n",
    "    # env.render()\n",
    "    if done:\n",
    "        print(\"info:\", info)\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,6))\n",
    "plt.cla()\n",
    "env.render_all()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.5 ('tensorflow24')",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "6fe0b1752bef58208e61c2c54ac686efbfd88b3c1c73b7f80dc2982421c99342"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}