render_episode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Render Episode\n",
    "Render a stored episode.  Env file needs to have \"episode\" and \"action\" keys. \n",
    "- creates a moving gif file of the episode\n",
    "- displays the episode in a widget with a slider for the time steps."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "baXcVq3ii0Cb"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 153
    },
    "colab_type": "code",
    "id": "eKL0JthzupFg",
    "outputId": "2ec78745-cb78-4426-ee9d-b8acac185910"
   },
   "outputs": [],
   "source": [
    "#!apt -qq install graphviz libgraphviz-dev pkg-config\n",
    "#!pip install -qq git+https://gitlab.aicrowd.com/flatland/flatland.git"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eSHpLxdt1jmE"
   },
   "outputs": [],
   "source": [
    "import PIL\n",
    "from flatland.utils.rendertools import RenderTool\n",
    "import imageio\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PU5GkH271guD"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:95% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import clear_output\n",
    "from IPython.core import display \n",
    "display.display(display.HTML(\"<style>.container { width:95% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eSHpLxdt1jmE"
   },
   "outputs": [],
   "source": [
    "def render_env(env_renderer):\n",
    "    ag0= env_renderer.env.agents[0]\n",
    "    #print(\"render_env ag0: \",ag0.position, ag0.direction)\n",
    "    aImage = env_renderer.render_env(show_rowcols=True, return_image=True)\n",
    "    pil_image = PIL.Image.fromarray(aImage)\n",
    "    return pil_image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "UeX1h4c0i5e6"
   },
   "source": [
    "# Experiments\n",
    "\n",
    "This has been mostly changed to load envs using `importlib_resources`.  It's getting them from the package \"envdata.tests`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PU5GkH271guD"
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "from flatland.envs.rail_env import RailEnv\n",
    "from flatland.envs.rail_generators import sparse_rail_generator\n",
    "from flatland.envs.line_generators import sparse_line_generator\n",
    "from flatland.envs.malfunction_generators import malfunction_from_file, no_malfunction_generator\n",
    "from flatland.envs.rail_generators import rail_from_file\n",
    "from flatland.envs.rail_env import RailEnvActions\n",
    "from flatland.envs.step_utils.states import TrainState"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from flatland.envs.persistence import RailEnvPersister"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...\n",
      "pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...\n",
      "pickle failed to load file: complex_scene_2.pkl  trying msgpack (deprecated)...\n",
      "This env file has no max_episode_steps (deprecated) - setting to 100\n"
     ]
    }
   ],
   "source": [
    "env, env_dict = RailEnvPersister.load_new(\"complex_scene_2.pkl\", load_from_package=\"env_data.railway\")\n",
    "_ = env.reset()\n",
    "env._max_episode_steps = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\u216993\\.conda\\envs\\flatland3-rl\\lib\\site-packages\\flatland\\utils\\rendertools.py:399: UserWarning: Predictor did not provide any predicted cells to render.                 Observation builder needs to populate: env.dev_obs_dict\n",
      "  Observation builder needs to populate: env.dev_obs_dict\")\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGBA size=704x284 at 0x1FFFCD7CE48>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the seed has to match that used to record the episode, in order for the malfunctions to match.\n",
    "oRT = RenderTool(env, show_debug=True)\n",
    "aImg = oRT.render_env(show_rowcols=True, return_image=True, show_inactive_agents=True)\n",
    "print(env._max_episode_steps)\n",
    "PIL.Image.fromarray(aImg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>initial_direction</th>\n",
       "      <th>direction</th>\n",
       "      <th>initial_position</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(11, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(12, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(14, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(18, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(14, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 5)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(13, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    initial_direction  direction initial_position position\n",
       "0                   1          1           (2, 1)     None\n",
       "1                   1          1           (1, 1)     None\n",
       "2                   1          1          (10, 4)     None\n",
       "3                   1          1          (11, 4)     None\n",
       "4                   1          1          (12, 4)     None\n",
       "5                   1          1          (13, 4)     None\n",
       "6                   1          1          (14, 4)     None\n",
       "7                   3          3         (18, 48)     None\n",
       "8                   3          3         (14, 48)     None\n",
       "9                   1          1          (13, 5)     None\n",
       "10                  3          3         (13, 48)     None"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loAgs = env_dict[\"agents\"]\n",
    "lCols =  \"initial_direction,direction,initial_position,position\".split(\",\")\n",
    "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
    "              for oAg in loAgs], columns=lCols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>initial_direction</th>\n",
       "      <th>direction</th>\n",
       "      <th>initial_position</th>\n",
       "      <th>position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(11, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(12, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(14, 4)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(18, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(14, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 5)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(13, 48)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    initial_direction  direction initial_position position\n",
       "0                   1          1           (2, 1)     None\n",
       "1                   1          1           (1, 1)     None\n",
       "2                   1          1          (10, 4)     None\n",
       "3                   1          1          (11, 4)     None\n",
       "4                   1          1          (12, 4)     None\n",
       "5                   1          1          (13, 4)     None\n",
       "6                   1          1          (14, 4)     None\n",
       "7                   3          3         (18, 48)     None\n",
       "8                   3          3         (14, 48)     None\n",
       "9                   1          1          (13, 5)     None\n",
       "10                  3          3         (13, 48)     None"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([ [getattr(oAg, sCol) for sCol in lCols] \n",
    "              for oAg in env.agents], columns=lCols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>initial_position</th>\n",
       "      <th>initial_direction</th>\n",
       "      <th>direction</th>\n",
       "      <th>target</th>\n",
       "      <th>moving</th>\n",
       "      <th>earliest_departure</th>\n",
       "      <th>latest_arrival</th>\n",
       "      <th>handle</th>\n",
       "      <th>speed_counter</th>\n",
       "      <th>action_saver</th>\n",
       "      <th>state_machine</th>\n",
       "      <th>malfunction_handler</th>\n",
       "      <th>position</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>old_direction</th>\n",
       "      <th>old_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 12)</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(19, 48)</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(10, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 46)</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>196</td>\n",
       "      <td>2</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(11, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(14, 46)</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>193</td>\n",
       "      <td>3</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(12, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(12, 42)</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>78</td>\n",
       "      <td>4</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(13, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(11, 42)</td>\n",
       "      <td>False</td>\n",
       "      <td>99</td>\n",
       "      <td>167</td>\n",
       "      <td>5</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(14, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 15)</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "      <td>60</td>\n",
       "      <td>6</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(18, 48)</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(1, 2)</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>199</td>\n",
       "      <td>7</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(14, 48)</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(11, 12)</td>\n",
       "      <td>False</td>\n",
       "      <td>83</td>\n",
       "      <td>147</td>\n",
       "      <td>8</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(13, 5)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 13)</td>\n",
       "      <td>False</td>\n",
       "      <td>28</td>\n",
       "      <td>55</td>\n",
       "      <td>9</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(13, 48)</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>(12, 12)</td>\n",
       "      <td>False</td>\n",
       "      <td>107</td>\n",
       "      <td>169</td>\n",
       "      <td>10</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   initial_position  initial_direction  direction    target  moving  \\\n",
       "0            (2, 1)                  1          1  (10, 12)   False   \n",
       "1            (1, 1)                  1          1  (19, 48)   False   \n",
       "2           (10, 4)                  1          1  (13, 46)   False   \n",
       "3           (11, 4)                  1          1  (14, 46)   False   \n",
       "4           (12, 4)                  1          1  (12, 42)   False   \n",
       "5           (13, 4)                  1          1  (11, 42)   False   \n",
       "6           (14, 4)                  1          1  (13, 15)   False   \n",
       "7          (18, 48)                  3          3    (1, 2)   False   \n",
       "8          (14, 48)                  3          3  (11, 12)   False   \n",
       "9           (13, 5)                  1          1  (13, 13)   False   \n",
       "10         (13, 48)                  3          3  (12, 12)   False   \n",
       "\n",
       "    earliest_departure  latest_arrival  handle  \\\n",
       "0                   64             191       0   \n",
       "1                   10             210       1   \n",
       "2                  121             196       2   \n",
       "3                  121             193       3   \n",
       "4                   10              78       4   \n",
       "5                   99             167       5   \n",
       "6                   31              60       6   \n",
       "7                    2             199       7   \n",
       "8                   83             147       8   \n",
       "9                   28              55       9   \n",
       "10                 107             169      10   \n",
       "\n",
       "                                        speed_counter  \\\n",
       "0   speed: 1.0                  max_count: 0      ...   \n",
       "1   speed: 1.0                  max_count: 0      ...   \n",
       "2   speed: 1.0                  max_count: 0      ...   \n",
       "3   speed: 1.0                  max_count: 0      ...   \n",
       "4   speed: 1.0                  max_count: 0      ...   \n",
       "5   speed: 1.0                  max_count: 0      ...   \n",
       "6   speed: 1.0                  max_count: 0      ...   \n",
       "7   speed: 1.0                  max_count: 0      ...   \n",
       "8   speed: 1.0                  max_count: 0      ...   \n",
       "9   speed: 1.0                  max_count: 0      ...   \n",
       "10  speed: 1.0                  max_count: 0      ...   \n",
       "\n",
       "                                  action_saver  \\\n",
       "0   is_action_saved: False, saved_action: None   \n",
       "1   is_action_saved: False, saved_action: None   \n",
       "2   is_action_saved: False, saved_action: None   \n",
       "3   is_action_saved: False, saved_action: None   \n",
       "4   is_action_saved: False, saved_action: None   \n",
       "5   is_action_saved: False, saved_action: None   \n",
       "6   is_action_saved: False, saved_action: None   \n",
       "7   is_action_saved: False, saved_action: None   \n",
       "8   is_action_saved: False, saved_action: None   \n",
       "9   is_action_saved: False, saved_action: None   \n",
       "10  is_action_saved: False, saved_action: None   \n",
       "\n",
       "                                        state_machine  \\\n",
       "0   \\n                  state: TrainState.WAITING ...   \n",
       "1   \\n                  state: TrainState.WAITING ...   \n",
       "2   \\n                  state: TrainState.WAITING ...   \n",
       "3   \\n                  state: TrainState.WAITING ...   \n",
       "4   \\n                  state: TrainState.WAITING ...   \n",
       "5   \\n                  state: TrainState.WAITING ...   \n",
       "6   \\n                  state: TrainState.WAITING ...   \n",
       "7   \\n                  state: TrainState.WAITING ...   \n",
       "8   \\n                  state: TrainState.WAITING ...   \n",
       "9   \\n                  state: TrainState.WAITING ...   \n",
       "10  \\n                  state: TrainState.WAITING ...   \n",
       "\n",
       "                                  malfunction_handler position arrival_time  \\\n",
       "0   malfunction_down_counter: 0                 in...     None         None   \n",
       "1   malfunction_down_counter: 0                 in...     None         None   \n",
       "2   malfunction_down_counter: 0                 in...     None         None   \n",
       "3   malfunction_down_counter: 0                 in...     None         None   \n",
       "4   malfunction_down_counter: 0                 in...     None         None   \n",
       "5   malfunction_down_counter: 0                 in...     None         None   \n",
       "6   malfunction_down_counter: 0                 in...     None         None   \n",
       "7   malfunction_down_counter: 0                 in...     None         None   \n",
       "8   malfunction_down_counter: 0                 in...     None         None   \n",
       "9   malfunction_down_counter: 0                 in...     None         None   \n",
       "10  malfunction_down_counter: 0                 in...     None         None   \n",
       "\n",
       "   old_direction old_position  \n",
       "0           None         None  \n",
       "1           None         None  \n",
       "2           None         None  \n",
       "3           None         None  \n",
       "4           None         None  \n",
       "5           None         None  \n",
       "6           None         None  \n",
       "7           None         None  \n",
       "8           None         None  \n",
       "9           None         None  \n",
       "10          None         None  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([ vars(oAg) for oAg in env.agents])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kkejL06T8xyU"
   },
   "outputs": [],
   "source": [
    "# from persistence.py\n",
    "def get_agent_state(env):\n",
    "    list_agents_state = []\n",
    "    for iAg, oAg in enumerate(env.agents):\n",
    "        # the int cast is to avoid numpy types which may cause problems with msgpack\n",
    "        # in env v2, agents may have position None, before starting\n",
    "        if oAg.position is None:\n",
    "            pos = (0, 0)\n",
    "        else:\n",
    "            pos = (int(oAg.position[0]), int(oAg.position[1]))\n",
    "        # print(\"pos:\", pos, type(pos[0]))\n",
    "        list_agents_state.append(\n",
    "            [*pos, int(oAg.direction), oAg.malfunction_handler])\n",
    "      \n",
    "    return list_agents_state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>initial_position</th>\n",
       "      <th>initial_direction</th>\n",
       "      <th>direction</th>\n",
       "      <th>target</th>\n",
       "      <th>moving</th>\n",
       "      <th>earliest_departure</th>\n",
       "      <th>latest_arrival</th>\n",
       "      <th>handle</th>\n",
       "      <th>speed_counter</th>\n",
       "      <th>action_saver</th>\n",
       "      <th>state_machine</th>\n",
       "      <th>malfunction_handler</th>\n",
       "      <th>position</th>\n",
       "      <th>arrival_time</th>\n",
       "      <th>old_direction</th>\n",
       "      <th>old_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(2, 1)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(10, 12)</td>\n",
       "      <td>False</td>\n",
       "      <td>64</td>\n",
       "      <td>191</td>\n",
       "      <td>0</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(1, 1)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(19, 48)</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>210</td>\n",
       "      <td>1</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(10, 4)</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>(13, 46)</td>\n",
       "      <td>False</td>\n",
       "      <td>121</td>\n",
       "      <td>196</td>\n",
       "      <td>2</td>\n",
       "      <td>speed: 1.0                  max_count: 0      ...</td>\n",
       "      <td>is_action_saved: False, saved_action: None</td>\n",
       "      <td>\\n                  state: TrainState.WAITING ...</td>\n",
       "      <td>malfunction_down_counter: 0                 in...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(11, 4)</td>\n",
       "      <td>1</td>\n",