shared_oa_ex2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f3af3147-99be-4557-92e4-bc7b7ebe1027",
   "metadata": {},
   "source": [
    "## Setup MDP environment and automaton based on LTL Specification "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "uniform-upgrade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from csrl.mdp import GridMDP\n",
    "import numpy as np \n",
    "from copy import deepcopy\n",
    "from csrl.oa import OmegaAutomaton\n",
    "\n",
    "shape = n_rows, n_cols = (4,4)\n",
    "\n",
    "structure = np.array([\n",
    "['E','B','E','E'],\n",
    "['E','B','E','E'],\n",
    "['E','B','E','E'],\n",
    "['T','B','T','E'],\n",
    "['E','E','E','E']\n",
    "])\n",
    "\n",
    "label = np.empty(shape,dtype=object)\n",
    "label.fill(())\n",
    "label[1,0] = ('a',)\n",
    "label[1,2] = ('b',)\n",
    "label[3,0] = ('g1',)\n",
    "label[3,2] = ('g2',)\n",
    "\n",
    "rew_structure =  np.empty(shape,dtype=float)\n",
    "rew_structure.fill(0.0)\n",
    "rew_structure[1,0] = 2\n",
    "rew_structure[1,2] = 2\n",
    "rew_structure[3,2] = 10\n",
    "rew_structure[3,0] = 10\n",
    "    \n",
    "grid_mdp = GridMDP(shape=shape, reward=rew_structure, structure=structure,label=label,figsize=5, p=0.8)\n",
    "grid_mdp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4259e08e-6d46-43a6-90ee-c45223335f4e",
   "metadata": {},
   "source": [
    "### Original LTL Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "modular-kidney",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Omega-automaton states (including the trap state): 4\n"
     ]
    },
    {
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      "text/plain": [
       "<csrl.oa.OmegaAutomaton at 0x7f1ee9786438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LTL Specification\n",
    "ltl = '((Fa & !g1) U (a & b)) & ((Fb & !g2) U (a&b)) & (FG g1) & (FG g2)'\n",
    "\n",
    "# Translate the LTL formula to an LDBA\n",
    "oa1 = OmegaAutomaton(ltl, extra_aps=grid_mdp.all_labels)\n",
    "\n",
    "print('Number of Omega-automaton states (including the trap state):',oa1.shape[1])\n",
    "display(oa1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f27441c-214d-4289-9e53-bd360bd1893c",
   "metadata": {},
   "source": [
    "### Alternate LTL Specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44a7fd76-afef-433d-8c01-c6b39e73490b",
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "<csrl.oa.OmegaAutomaton at 0x7f1ee97ad518>"
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     },
     "metadata": {},
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   ],
   "source": [
    "ltl2 = 'F( (a&b) & XF ( (g1 | XF g1) &  ( g2 | XF g2)) )'\n",
    "oa2 = OmegaAutomaton(ltl2, extra_aps=grid_mdp.all_labels)\n",
    "display(oa2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79b5b17b-7caa-4c1d-ba5d-49f7790ca0e6",
   "metadata": {},
   "source": [
    "### Compute pair product MDP (agent x MDP). \n",
    "Use oa2 for alternate specification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "relative-philosophy",
   "metadata": {},
   "outputs": [],
   "source": [
    "from csrl import ControlSynthesis\n",
    "import numpy as np \n",
    "\n",
    "# Construct product MDPs\n",
    "csrl1 = ControlSynthesis(grid_mdp,oa1)\n",
    "\n",
    "csrl2 = ControlSynthesis(grid_mdp,oa1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "brief-middle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 1, 4, 4, 4, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from multi import MultiControlSynthesis\n",
    "\n",
    "mcsrl = MultiControlSynthesis([csrl1, csrl2], oa = oa1, starts=[(0, 0), (0, 3)], mdp=grid_mdp, sharedoa=True)\n",
    "mcsrl.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cd636b-e850-4bee-9d2c-6d5abe3d306e",
   "metadata": {},
   "source": [
    "### Multi-agent Reward Shaping \n",
    "Episodes > 30k "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0a438ecc-eb6b-47d5-811f-feb8e82f6fa6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.signal import savgol_filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "technological-donor",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from logger import Trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "348f775c-1f29-4e3f-85e3-9fa9bf923a6a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode :0\n",
      "episode :7500\n",
      "episode :15000\n",
      "episode :22500\n",
      "episode :30000\n",
      "episode :37500\n",
      "episode :45000\n",
      "episode :52500\n",
      "episode :60000\n",
      "episode :67500\n"
     ]
    }
   ],
   "source": [
    "qval, ep_returns, trace = mcsrl.combined_qlearning(T=70,offset=10, K=75000, debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1339e9de-4c35-4995-adb8-b37d8ff39f7a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iteration</th>\n",
       "      <th>episode</th>\n",
       "      <th>step</th>\n",
       "      <th>labels</th>\n",
       "      <th>labels_seen</th>\n",
       "      <th>state_0</th>\n",
       "      <th>state_1</th>\n",
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       "      <th>reward_1</th>\n",
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       "      <th>action_1</th>\n",
       "      <th>available_actions_0</th>\n",
       "      <th>available_actions_1</th>\n",
       "    </tr>\n",
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       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
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       "      <td>(g1, g2)</td>\n",
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       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>1</td>\n",
       "      <td>(a,)</td>\n",
       "      <td>{a}</td>\n",
       "      <td>[0, 0, 1, 0]</td>\n",
       "      <td>[0, 0, 0, 2]</td>\n",
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       "      <td>[0, 1, 2, 3]</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>2</td>\n",
       "      <td>(b,)</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 1, 2]</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>[0, 1, 2, 3]</td>\n",
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       "      <td>(b,)</td>\n",
       "      <td>{a, b}</td>\n",
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       "      <td>[0, 0, 1, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>4</td>\n",
       "      <td>(a,)</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 0, 1, 0]</td>\n",
       "      <td>[0, 0, 2, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>5</td>\n",
       "      <td>(a, b)</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 1, 1, 0]</td>\n",
       "      <td>[0, 1, 1, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>6</td>\n",
       "      <td>()</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 1, 2, 0]</td>\n",
       "      <td>[0, 1, 2, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>7</td>\n",
       "      <td>(g2,)</td>\n",
       "      <td>{a, g2, b}</td>\n",
       "      <td>[0, 1, 2, 0]</td>\n",
       "      <td>[0, 1, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
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       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>8</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 1, 3, 0]</td>\n",
       "      <td>[0, 1, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[6]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>9</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 2, 3, 0]</td>\n",
       "      <td>[0, 2, 3, 2]</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>10</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>11</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>12</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>13</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>14</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>15</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>16</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>17</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>18</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>19</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>20</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>21</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>22</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>23</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[2]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0</td>\n",
       "      <td>74991</td>\n",
       "      <td>24</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 3, 3, 0]</td>\n",
       "      <td>[0, 3, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iteration episode step    labels     labels_seen       state_0  \\\n",
       "0          0   74991    0  (g1, g2)              {}  [0, 0, 0, 0]   \n",
       "1          0   74991    1      (a,)             {a}  [0, 0, 1, 0]   \n",
       "2          0   74991    2      (b,)          {a, b}  [0, 0, 0, 0]   \n",
       "3          0   74991    3      (b,)          {a, b}  [0, 0, 0, 0]   \n",
       "4          0   74991    4      (a,)          {a, b}  [0, 0, 1, 0]   \n",
       "5          0   74991    5    (a, b)          {a, b}  [0, 1, 1, 0]   \n",
       "6          0   74991    6        ()          {a, b}  [0, 1, 2, 0]   \n",
       "7          0   74991    7     (g2,)      {a, g2, b}  [0, 1, 2, 0]   \n",
       "8          0   74991    8  (g1, g2)  {g1, a, g2, b}  [0, 1, 3, 0]   \n",
       "9          0   74991    9  (g1, g2)  {g1, a, g2, b}  [0, 2, 3, 0]   \n",
       "10         0   74991   10  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "11         0   74991   11  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "12         0   74991   12  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "13         0   74991   13  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "14         0   74991   14  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "15         0   74991   15  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "16         0   74991   16  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "17         0   74991   17  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "18         0   74991   18  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "19         0   74991   19  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "20         0   74991   20  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "21         0   74991   21  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "22         0   74991   22  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "23         0   74991   23  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "24         0   74991   24  (g1, g2)  {g1, a, g2, b}  [0, 3, 3, 0]   \n",
       "\n",
       "         state_1  reward_0  reward_1 action_0 action_1 available_actions_0  \\\n",
       "0   [0, 0, 0, 3]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "1   [0, 0, 0, 2]      0.00      0.00      [0]      [1]        [0, 1, 2, 3]   \n",
       "2   [0, 0, 1, 2]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "3   [0, 0, 1, 2]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "4   [0, 0, 2, 2]      0.00      0.00      [0]      [0]        [0, 1, 2, 3]   \n",
       "5   [0, 1, 1, 2]      0.00      0.00      [1]      [1]     [0, 1, 2, 3, 6]   \n",
       "6   [0, 1, 2, 2]      0.00      0.00      [1]      [1]     [0, 1, 2, 3, 6]   \n",
       "7   [0, 1, 3, 2]      0.00      0.00      [1]      [3]     [0, 1, 2, 3, 6]   \n",
       "8   [0, 1, 3, 2]      0.00      0.00      [6]      [3]     [0, 1, 2, 3, 6]   \n",
       "9   [0, 2, 3, 2]      0.01      0.01      [0]      [0]        [0, 1, 2, 3]   \n",
       "10  [0, 3, 3, 2]      0.00      0.00      [1]      [0]        [0, 1, 2, 3]   \n",
       "11  [0, 3, 3, 2]      0.00      0.00      [1]      [1]        [0, 1, 2, 3]   \n",
       "12  [0, 3, 3, 2]      0.00      0.00      [0]      [0]        [0, 1, 2, 3]   \n",
       "13  [0, 3, 3, 2]      0.00      0.00      [3]      [0]        [0, 1, 2, 3]   \n",
       "14  [0, 3, 3, 2]      0.00      0.00      [0]      [3]        [0, 1, 2, 3]   \n",
       "15  [0, 3, 3, 2]      0.00      0.00      [2]      [3]        [0, 1, 2, 3]   \n",
       "16  [0, 3, 3, 2]      0.00      0.00      [0]      [0]        [0, 1, 2, 3]   \n",
       "17  [0, 3, 3, 2]      0.00      0.00      [1]      [0]        [0, 1, 2, 3]   \n",
       "18  [0, 3, 3, 2]      0.00      0.00      [3]      [1]        [0, 1, 2, 3]   \n",
       "19  [0, 3, 3, 2]      0.00      0.00      [2]      [2]        [0, 1, 2, 3]   \n",
       "20  [0, 3, 3, 2]      0.00      0.00      [3]      [2]        [0, 1, 2, 3]   \n",
       "21  [0, 3, 3, 2]      0.00      0.00      [0]      [2]        [0, 1, 2, 3]   \n",
       "22  [0, 3, 3, 2]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "23  [0, 3, 3, 2]      0.00      0.00      [2]      [3]        [0, 1, 2, 3]   \n",
       "24  [0, 3, 3, 2]      0.00      0.00      [1]      [0]        [0, 1, 2, 3]   \n",
       "\n",
       "   available_actions_1  \n",
       "0         [0, 1, 2, 3]  \n",
       "1         [0, 1, 2, 3]  \n",
       "2         [0, 1, 2, 3]  \n",
       "3         [0, 1, 2, 3]  \n",
       "4         [0, 1, 2, 3]  \n",
       "5      [0, 1, 2, 3, 6]  \n",
       "6      [0, 1, 2, 3, 6]  \n",
       "7      [0, 1, 2, 3, 6]  \n",
       "8      [0, 1, 2, 3, 6]  \n",
       "9         [0, 1, 2, 3]  \n",
       "10        [0, 1, 2, 3]  \n",
       "11        [0, 1, 2, 3]  \n",
       "12        [0, 1, 2, 3]  \n",
       "13        [0, 1, 2, 3]  \n",
       "14        [0, 1, 2, 3]  \n",
       "15        [0, 1, 2, 3]  \n",
       "16        [0, 1, 2, 3]  \n",
       "17        [0, 1, 2, 3]  \n",
       "18        [0, 1, 2, 3]  \n",
       "19        [0, 1, 2, 3]  \n",
       "20        [0, 1, 2, 3]  \n",
       "21        [0, 1, 2, 3]  \n",
       "22        [0, 1, 2, 3]  \n",
       "23        [0, 1, 2, 3]  \n",
       "24        [0, 1, 2, 3]  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trace.df.head(n=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "9b478688-e7ce-4755-b376-0f256f052fb6",
   "metadata": {},
   "outputs": [],
   "source": [
    "policies = [np.argmax(qval[0],axis=4), np.argmax(qval[1],axis=4)]\n",
    "values = [np.max(qval[0],axis=4), np.max(qval[1],axis=4)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7c893d0-0726-41f3-a5d9-d369f25d20e0",
   "metadata": {},
   "source": [
    "#### Plot results and smooth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d9992f51-3d96-4224-b107-d9d7d7b18b10",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'return per episode')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "plt.plot( ep_returns[:,0], color='c', label='agent 0', alpha=0.2)\n",
    "yhat = savgol_filter(ep_returns[:,0], 101, 3) # window size 101, polynomial order 3\n",
    "yhat2 = savgol_filter(ep_returns[:,1], 101, 3)\n",
    "plt.plot(yhat, color='b', label='smooth')\n",
    "# plt.plot(yhat2, color='g', label='smooth2')\n",
    "# plt.plot(np.arange(0, ep_returns.shape[0]), ep_returns[:,1], color='g',  label='agent 1')\n",
    "\n",
    "# Naming the x-axis, y-axis and the whole graph\n",
    "plt.xlabel(\"Episodes\")\n",
    "plt.ylabel(\"Acc Return\")\n",
    "plt.title(\"return per episode\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bab2d6e2-e849-4e36-9ab2-14e77e5d78f4",
   "metadata": {},
   "source": [
    "#### Save trace and returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "31896ef0-cedd-4440-bc3f-370876997397",
   "metadata": {},
   "outputs": [],
   "source": [
    "trace.save('shared_oa_ex2_returns_df.csv')\n",
    "np.savetxt('shared_oa_ex2_returns.csv', ep_returns, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "eb9d7ebd-fb17-4ab0-bec7-b87493c02984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "28a728fb62014d829bae3321d32949de",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=0, description='i', max=0), IntSlider(value=0, description='q', max=3), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcsrl.plot(1, policies[1], values[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "468637bd-4272-4c28-99a6-51737fe7093e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Baseline: Independent QL "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "311ef8fc-f0da-41fc-9108-4f6be5cab985",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 4, 4, 4)\n"
     ]
    }
   ],
   "source": [
    "qval_base, ep_returns_base, base_trace = mcsrl.combined_qlearning_noshaping(T=70, K=75000, map= 'bench1', debug=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d6650a7-dfcb-416a-aed8-77315883b733",
   "metadata": {},
   "outputs": [],
   "source": [
    "policies_b = [np.argmax(qval_base[0],axis=2), np.argmax(qval_base[1],axis=2)]\n",
    "values_b = [np.max(qval_base[0],axis=2), np.max(qval_base[1],axis=2)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60302fa1-e86f-40a8-b06f-33464711f9c6",
   "metadata": {},
   "source": [
    "#### Plot and smooth results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6b26e056-1d43-42ef-b1bc-5bb9b98c7ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'return per episode')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14, 10))\n",
    "plt.plot( ep_returns_base[:,0], color='c', label='agent 0', alpha=0.2)\n",
    "# plt.plot( ep_returns_base[:,0], color='m', label='agent 0', alpha=0.2)\n",
    "yhat = savgol_filter(ep_returns_base[:,0], 201, 3) # window size 101, polynomial order 3\n",
    "yhat2 = savgol_filter(ep_returns_base[:,1], 201, 3)\n",
    "plt.plot(yhat, color='b', label='smooth')\n",
    "plt.plot(yhat2, color='g', label='smooth2')\n",
    "# plt.plot(np.arange(0, ep_returns.shape[0]), ep_returns[:,1], color='g',  label='agent 1')\n",
    "\n",
    "# Naming the x-axis, y-axis and the whole graph\n",
    "plt.xlabel(\"Episodes\")\n",
    "plt.ylabel(\"Acc Return\")\n",
    "plt.title(\"return per episode\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85a8fac6-354a-4ac3-a260-f942802d393f",
   "metadata": {},
   "outputs": [],
   "source": [
    "base_trace.save('shared_oa_ex2_returns_df_base.csv')\n",
    "np.savetxt('shared_oa_ex2_returns_base.csv', ep_returns_base, delimiter=',')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73a64f09-e203-475d-a62b-5ad5c248bb0c",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Plotting & video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a77d6dc1-c23b-449b-b49e-4f2f5bb08133",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotter import Plotter\n",
    "from annotation import Annotation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "255b8a57-38bf-4afa-a8fb-c82a81905a4b",
   "metadata": {},
   "source": [
    "#### Create annotation class for video plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "13ef902c-edb1-4baf-89f4-f33aa78cbb81",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotation = Annotation(grid_mdp, oa1, interactive= ['a', 'b'], targets=['g1', 'g2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "96111e2d-1d31-4570-aa4b-8e1180de3b5a",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "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>iteration</th>\n",
       "      <th>episode</th>\n",
       "      <th>step</th>\n",
       "      <th>labels</th>\n",
       "      <th>labels_seen</th>\n",
       "      <th>state_0</th>\n",
       "      <th>state_1</th>\n",
       "      <th>reward_0</th>\n",
       "      <th>reward_1</th>\n",
       "      <th>action_0</th>\n",
       "      <th>action_1</th>\n",
       "      <th>available_actions_0</th>\n",
       "      <th>available_actions_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>0</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{}</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 0, 3]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>1</td>\n",
       "      <td>(a,)</td>\n",
       "      <td>{a}</td>\n",
       "      <td>[0, 0, 1, 0]</td>\n",
       "      <td>[0, 0, 0, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>2</td>\n",
       "      <td>(b,)</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 0, 0, 0]</td>\n",
       "      <td>[0, 0, 1, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>3</td>\n",
       "      <td>(a, b)</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 1, 1, 0]</td>\n",
       "      <td>[0, 1, 1, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>4</td>\n",
       "      <td>()</td>\n",
       "      <td>{a, b}</td>\n",
       "      <td>[0, 1, 2, 0]</td>\n",
       "      <td>[0, 1, 2, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[1]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>5</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 1, 3, 0]</td>\n",
       "      <td>[0, 1, 3, 2]</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>[6]</td>\n",
       "      <td>[3]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "      <td>[0, 1, 2, 3, 6]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>0</td>\n",
       "      <td>74995</td>\n",
       "      <td>6</td>\n",
       "      <td>(g1, g2)</td>\n",
       "      <td>{g1, a, g2, b}</td>\n",
       "      <td>[0, 2, 3, 0]</td>\n",
       "      <td>[0, 2, 3, 2]</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.01</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "      <td>[0, 1, 2, 3]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration episode step    labels     labels_seen       state_0  \\\n",
       "280         0   74995    0  (g1, g2)              {}  [0, 0, 0, 0]   \n",
       "281         0   74995    1      (a,)             {a}  [0, 0, 1, 0]   \n",
       "282         0   74995    2      (b,)          {a, b}  [0, 0, 0, 0]   \n",
       "283         0   74995    3    (a, b)          {a, b}  [0, 1, 1, 0]   \n",
       "284         0   74995    4        ()          {a, b}  [0, 1, 2, 0]   \n",
       "285         0   74995    5  (g1, g2)  {g1, a, g2, b}  [0, 1, 3, 0]   \n",
       "286         0   74995    6  (g1, g2)  {g1, a, g2, b}  [0, 2, 3, 0]   \n",
       "\n",
       "          state_1  reward_0  reward_1 action_0 action_1 available_actions_0  \\\n",
       "280  [0, 0, 0, 3]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "281  [0, 0, 0, 2]      0.00      0.00      [0]      [1]        [0, 1, 2, 3]   \n",
       "282  [0, 0, 1, 2]      0.00      0.00      [1]      [3]        [0, 1, 2, 3]   \n",
       "283  [0, 1, 1, 2]      0.00      0.00      [1]      [1]     [0, 1, 2, 3, 6]   \n",
       "284  [0, 1, 2, 2]      0.00      0.00      [1]      [1]     [0, 1, 2, 3, 6]   \n",
       "285  [0, 1, 3, 2]      0.00      0.00      [6]      [3]     [0, 1, 2, 3, 6]   \n",
       "286  [0, 2, 3, 2]      0.01      0.01      [0]      [0]        [0, 1, 2, 3]   \n",
       "\n",
       "    available_actions_1  \n",
       "280        [0, 1, 2, 3]  \n",
       "281        [0, 1, 2, 3]  \n",
       "282        [0, 1, 2, 3]  \n",
       "283     [0, 1, 2, 3, 6]  \n",
       "284     [0, 1, 2, 3, 6]  \n",
       "285     [0, 1, 2, 3, 6]  \n",
       "286        [0, 1, 2, 3]  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_t=trace.get_episode(74995)\n",
    "df_t.head(n=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "3ce2eb4b-5f83-422c-833c-2476e5fb95d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "video_recorder = Plotter(annotation, grid_mdp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "484bf6cb-f9c5-4685-9d8e-9546fb03a515",
   "metadata": {},
   "source": [
    "#### Save episode to video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "56786aac-72d6-41d5-b40a-73dbc1e18da8",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7/7 [00:00<00:00, 21.39it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "video_recorder.record('sharedoa2/shared_oa_ex2_maptest_returns.mp4', trace.get_episode(74995).head(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec83815d-676f-4cc7-9bb8-4e7e326e94fe",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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