Hopper-v4-Done.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import json\n",
    "import os\n",
    "import seaborn as sns\n",
    "from numpy import median\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RESULTS USING THE SIGNAL OF DONE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11394, 17)\n"
     ]
    }
   ],
   "source": [
    "env='Hopper-v4'\n",
    "df_comper = pd.DataFrame()\n",
    "for i in range(1,6):\n",
    "  path ='COMPER_DDPG/data_using_done/'+env+'/train/trial'+str(i)+'/progress.json'\n",
    "  aux = pd.read_json(path,lines=True)    \n",
    "  df_comper = pd.concat([df_comper,aux])\n",
    "df_comper[\"Method\"]=\"COMPER-DDPG\"\n",
    "print(df_comper.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19036, 17)\n"
     ]
    }
   ],
   "source": [
    "df_ddpg = pd.DataFrame()\n",
    "for i in range(1,6):\n",
    "  path ='DPPG/data_using_done/'+env+'/train/trial'+str(i)+'/progress.json'\n",
    "  aux = pd.read_json(path,lines=True)    \n",
    "  df_ddpg = pd.concat([df_ddpg,aux])\n",
    "df_ddpg[\"Method\"]=\"DDPG\"\n",
    "print(df_ddpg.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Trial</th>\n",
       "      <th>LogCount</th>\n",
       "      <th>Task</th>\n",
       "      <th>Time</th>\n",
       "      <th>TotalItr</th>\n",
       "      <th>TMCount</th>\n",
       "      <th>RTMCount</th>\n",
       "      <th>e</th>\n",
       "      <th>Ep</th>\n",
       "      <th>EpItr</th>\n",
       "      <th>Done</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>AvgEp</th>\n",
       "      <th>Avg100Ep</th>\n",
       "      <th>AvgLast50Ep</th>\n",
       "      <th>AvgLast10Ep</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:47:52</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>True</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:50:05</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>True</td>\n",
       "      <td>105.386270</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:52:20</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>0.008350</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>True</td>\n",
       "      <td>89.612832</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:53:43</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0.009941</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>55.340243</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:53:48</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0.010040</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>58.396770</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trial  LogCount       Task                 Time  TotalItr  TMCount  \\\n",
       "0      1         1  Hopper-v4  25-09-2023 09:47:52        59        0   \n",
       "1      1         2  Hopper-v4  25-09-2023 09:50:05       115        0   \n",
       "2      1         3  Hopper-v4  25-09-2023 09:52:20       168        0   \n",
       "3      1         4  Hopper-v4  25-09-2023 09:53:43       200        0   \n",
       "4      1         5  Hopper-v4  25-09-2023 09:53:48       202        0   \n",
       "\n",
       "   RTMCount         e  Ep  EpItr   Done       EpRew      AvgEp   Avg100Ep  \\\n",
       "0        59  0.002933   1     60   True   86.680591  86.680591  86.680591   \n",
       "1       115  0.005716   2     57   True  105.386270  96.033430  96.033430   \n",
       "2       168  0.008350   3     54   True   89.612832  93.893231  93.893231   \n",
       "3       200  0.009941   4     33  False   55.340243  84.254984  84.254984   \n",
       "4       202  0.010040   4     35   True   58.396770  79.083341  79.083341   \n",
       "\n",
       "   AvgLast50Ep  AvgLast10Ep       Method  \n",
       "0    86.680591    86.680591  COMPER-DDPG  \n",
       "1    96.033430    96.033430  COMPER-DDPG  \n",
       "2    93.893231    93.893231  COMPER-DDPG  \n",
       "3    84.254984    84.254984  COMPER-DDPG  \n",
       "4    79.083341    79.083341  COMPER-DDPG  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comper.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Trial</th>\n",
       "      <th>LogCount</th>\n",
       "      <th>Task</th>\n",
       "      <th>Time</th>\n",
       "      <th>TotalItr</th>\n",
       "      <th>TMCount</th>\n",
       "      <th>RTMCount</th>\n",
       "      <th>e</th>\n",
       "      <th>Ep</th>\n",
       "      <th>EpItr</th>\n",
       "      <th>Done</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>AvgEp</th>\n",
       "      <th>Avg100Ep</th>\n",
       "      <th>AvgLast50Ep</th>\n",
       "      <th>AvgLast10Ep</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 04:10:47</td>\n",
       "      <td>66</td>\n",
       "      <td>67</td>\n",
       "      <td>0</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>1</td>\n",
       "      <td>67</td>\n",
       "      <td>True</td>\n",
       "      <td>86.356344</td>\n",
       "      <td>86.356344</td>\n",
       "      <td>86.356344</td>\n",
       "      <td>86.356344</td>\n",
       "      <td>86.356344</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 04:10:53</td>\n",
       "      <td>113</td>\n",
       "      <td>114</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005617</td>\n",
       "      <td>2</td>\n",
       "      <td>48</td>\n",
       "      <td>True</td>\n",
       "      <td>81.375474</td>\n",
       "      <td>83.865909</td>\n",
       "      <td>83.865909</td>\n",
       "      <td>83.865909</td>\n",
       "      <td>83.865909</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 04:10:54</td>\n",
       "      <td>120</td>\n",
       "      <td>121</td>\n",
       "      <td>0</td>\n",
       "      <td>0.005964</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>3.689655</td>\n",
       "      <td>57.140491</td>\n",
       "      <td>57.140491</td>\n",
       "      <td>57.140491</td>\n",
       "      <td>57.140491</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 04:10:55</td>\n",
       "      <td>127</td>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006312</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>3.705186</td>\n",
       "      <td>43.781665</td>\n",
       "      <td>43.781665</td>\n",
       "      <td>43.781665</td>\n",
       "      <td>43.781665</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 04:10:56</td>\n",
       "      <td>134</td>\n",
       "      <td>135</td>\n",
       "      <td>0</td>\n",
       "      <td>0.006660</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>3.782121</td>\n",
       "      <td>35.781756</td>\n",
       "      <td>35.781756</td>\n",
       "      <td>35.781756</td>\n",
       "      <td>35.781756</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trial  LogCount       Task                 Time  TotalItr  TMCount  \\\n",
       "0      1         1  Hopper-v4  04-10-2023 04:10:47        66       67   \n",
       "1      1         2  Hopper-v4  04-10-2023 04:10:53       113      114   \n",
       "2      1         3  Hopper-v4  04-10-2023 04:10:54       120      121   \n",
       "3      1         4  Hopper-v4  04-10-2023 04:10:55       127      128   \n",
       "4      1         5  Hopper-v4  04-10-2023 04:10:56       134      135   \n",
       "\n",
       "   RTMCount         e  Ep  EpItr  Done      EpRew      AvgEp   Avg100Ep  \\\n",
       "0         0  0.003280   1     67  True  86.356344  86.356344  86.356344   \n",
       "1         0  0.005617   2     48  True  81.375474  83.865909  83.865909   \n",
       "2         0  0.005964   3      8  True   3.689655  57.140491  57.140491   \n",
       "3         0  0.006312   4      8  True   3.705186  43.781665  43.781665   \n",
       "4         0  0.006660   5      8  True   3.782121  35.781756  35.781756   \n",
       "\n",
       "   AvgLast50Ep  AvgLast10Ep Method  \n",
       "0    86.356344    86.356344   DDPG  \n",
       "1    83.865909    83.865909   DDPG  \n",
       "2    57.140491    57.140491   DDPG  \n",
       "3    43.781665    43.781665   DDPG  \n",
       "4    35.781756    35.781756   DDPG  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ddpg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Trial</th>\n",
       "      <th>LogCount</th>\n",
       "      <th>Task</th>\n",
       "      <th>Time</th>\n",
       "      <th>TotalItr</th>\n",
       "      <th>TMCount</th>\n",
       "      <th>RTMCount</th>\n",
       "      <th>e</th>\n",
       "      <th>Ep</th>\n",
       "      <th>EpItr</th>\n",
       "      <th>Done</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>AvgEp</th>\n",
       "      <th>Avg100Ep</th>\n",
       "      <th>AvgLast50Ep</th>\n",
       "      <th>AvgLast10Ep</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:47:52</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "      <td>0.002933</td>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>True</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>86.680591</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:50:05</td>\n",
       "      <td>115</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>0.005716</td>\n",
       "      <td>2</td>\n",
       "      <td>57</td>\n",
       "      <td>True</td>\n",
       "      <td>105.386270</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>96.033430</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:52:20</td>\n",
       "      <td>168</td>\n",
       "      <td>0</td>\n",
       "      <td>168</td>\n",
       "      <td>0.008350</td>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "      <td>True</td>\n",
       "      <td>89.612832</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>93.893231</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:53:43</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>200</td>\n",
       "      <td>0.009941</td>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "      <td>False</td>\n",
       "      <td>55.340243</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>84.254984</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25-09-2023 09:53:48</td>\n",
       "      <td>202</td>\n",
       "      <td>0</td>\n",
       "      <td>202</td>\n",
       "      <td>0.010040</td>\n",
       "      <td>4</td>\n",
       "      <td>35</td>\n",
       "      <td>True</td>\n",
       "      <td>58.396770</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>79.083341</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trial  LogCount       Task                 Time  TotalItr  TMCount  \\\n",
       "0      1         1  Hopper-v4  25-09-2023 09:47:52        59        0   \n",
       "1      1         2  Hopper-v4  25-09-2023 09:50:05       115        0   \n",
       "2      1         3  Hopper-v4  25-09-2023 09:52:20       168        0   \n",
       "3      1         4  Hopper-v4  25-09-2023 09:53:43       200        0   \n",
       "4      1         5  Hopper-v4  25-09-2023 09:53:48       202        0   \n",
       "\n",
       "   RTMCount         e  Ep  EpItr   Done       EpRew      AvgEp   Avg100Ep  \\\n",
       "0        59  0.002933   1     60   True   86.680591  86.680591  86.680591   \n",
       "1       115  0.005716   2     57   True  105.386270  96.033430  96.033430   \n",
       "2       168  0.008350   3     54   True   89.612832  93.893231  93.893231   \n",
       "3       200  0.009941   4     33  False   55.340243  84.254984  84.254984   \n",
       "4       202  0.010040   4     35   True   58.396770  79.083341  79.083341   \n",
       "\n",
       "   AvgLast50Ep  AvgLast10Ep       Method  \n",
       "0    86.680591    86.680591  COMPER-DDPG  \n",
       "1    96.033430    96.033430  COMPER-DDPG  \n",
       "2    93.893231    93.893231  COMPER-DDPG  \n",
       "3    84.254984    84.254984  COMPER-DDPG  \n",
       "4    79.083341    79.083341  COMPER-DDPG  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.concat([df_comper,df_ddpg])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Trial</th>\n",
       "      <th>LogCount</th>\n",
       "      <th>Task</th>\n",
       "      <th>Time</th>\n",
       "      <th>TotalItr</th>\n",
       "      <th>TMCount</th>\n",
       "      <th>RTMCount</th>\n",
       "      <th>e</th>\n",
       "      <th>Ep</th>\n",
       "      <th>EpItr</th>\n",
       "      <th>Done</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>AvgEp</th>\n",
       "      <th>Avg100Ep</th>\n",
       "      <th>AvgLast50Ep</th>\n",
       "      <th>AvgLast10Ep</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>5</td>\n",
       "      <td>4677</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 14:33:03</td>\n",
       "      <td>49978</td>\n",
       "      <td>49867</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>4443</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>4.188887</td>\n",
       "      <td>13.806324</td>\n",
       "      <td>4.655494</td>\n",
       "      <td>4.041371</td>\n",
       "      <td>4.281275</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>5</td>\n",
       "      <td>4678</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 14:33:05</td>\n",
       "      <td>49985</td>\n",
       "      <td>49874</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>4444</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>4.219903</td>\n",
       "      <td>13.804275</td>\n",
       "      <td>4.640064</td>\n",
       "      <td>4.043964</td>\n",
       "      <td>4.155398</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4678</th>\n",
       "      <td>5</td>\n",
       "      <td>4679</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 14:33:06</td>\n",
       "      <td>49992</td>\n",
       "      <td>49881</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>4445</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>4.254085</td>\n",
       "      <td>13.802234</td>\n",
       "      <td>4.613485</td>\n",
       "      <td>4.046388</td>\n",
       "      <td>4.093917</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>5</td>\n",
       "      <td>4680</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 14:33:07</td>\n",
       "      <td>49999</td>\n",
       "      <td>49888</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>4446</td>\n",
       "      <td>8</td>\n",
       "      <td>True</td>\n",
       "      <td>4.300753</td>\n",
       "      <td>13.800204</td>\n",
       "      <td>4.584645</td>\n",
       "      <td>4.050016</td>\n",
       "      <td>4.006930</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>5</td>\n",
       "      <td>4681</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>04-10-2023 14:33:07</td>\n",
       "      <td>50000</td>\n",
       "      <td>49889</td>\n",
       "      <td>0</td>\n",
       "      <td>0.999</td>\n",
       "      <td>4447</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>0.946108</td>\n",
       "      <td>13.797458</td>\n",
       "      <td>4.520955</td>\n",
       "      <td>3.988122</td>\n",
       "      <td>3.692179</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Trial  LogCount       Task                 Time  TotalItr  TMCount  \\\n",
       "4676      5      4677  Hopper-v4  04-10-2023 14:33:03     49978    49867   \n",
       "4677      5      4678  Hopper-v4  04-10-2023 14:33:05     49985    49874   \n",
       "4678      5      4679  Hopper-v4  04-10-2023 14:33:06     49992    49881   \n",
       "4679      5      4680  Hopper-v4  04-10-2023 14:33:07     49999    49888   \n",
       "4680      5      4681  Hopper-v4  04-10-2023 14:33:07     50000    49889   \n",
       "\n",
       "      RTMCount      e    Ep  EpItr  Done     EpRew      AvgEp  Avg100Ep  \\\n",
       "4676         0  0.999  4443      8  True  4.188887  13.806324  4.655494   \n",
       "4677         0  0.999  4444      8  True  4.219903  13.804275  4.640064   \n",
       "4678         0  0.999  4445      8  True  4.254085  13.802234  4.613485   \n",
       "4679         0  0.999  4446      8  True  4.300753  13.800204  4.584645   \n",
       "4680         0  0.999  4447      2  True  0.946108  13.797458  4.520955   \n",
       "\n",
       "      AvgLast50Ep  AvgLast10Ep Method  \n",
       "4676     4.041371     4.281275   DDPG  \n",
       "4677     4.043964     4.155398   DDPG  \n",
       "4678     4.046388     4.093917   DDPG  \n",
       "4679     4.050016     4.006930   DDPG  \n",
       "4680     3.988122     3.692179   DDPG  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TotalItr', ylabel='AvgLast10Ep'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1500x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(nrows=2,ncols=2)\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(10)\n",
    "sns.lineplot(data=df, x=\"TotalItr\", y=\"EpRew\",hue='Method',ax=axs[0][0])\n",
    "sns.lineplot(data=df, x=\"TotalItr\", y=\"Avg100Ep\",hue=\"Method\",ax=axs[0][1])\n",
    "sns.lineplot(data=df, x=\"TotalItr\", y=\"AvgLast50Ep\",hue=\"Method\",ax=axs[1][0])\n",
    "sns.lineplot(data=df, x=\"TotalItr\", y=\"AvgLast10Ep\",hue=\"Method\",ax=axs[1][1])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 6)\n"
     ]
    },
    {
     "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>Trial</th>\n",
       "      <th>Time</th>\n",
       "      <th>Task</th>\n",
       "      <th>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>25-09-2023 13:19:37</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>5000</td>\n",
       "      <td>6.486307</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>25-09-2023 16:56:39</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>10000</td>\n",
       "      <td>6.441560</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>25-09-2023 20:34:53</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>15000</td>\n",
       "      <td>6.431958</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>26-09-2023 00:12:47</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>20000</td>\n",
       "      <td>6.497798</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>26-09-2023 03:51:32</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25000</td>\n",
       "      <td>6.488228</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trial                 Time       Task    Int  EvalAvgRew       Method\n",
       "0      1  25-09-2023 13:19:37  Hopper-v4   5000    6.486307  COMPER-DDPG\n",
       "1      1  25-09-2023 16:56:39  Hopper-v4  10000    6.441560  COMPER-DDPG\n",
       "2      1  25-09-2023 20:34:53  Hopper-v4  15000    6.431958  COMPER-DDPG\n",
       "3      1  26-09-2023 00:12:47  Hopper-v4  20000    6.497798  COMPER-DDPG\n",
       "4      1  26-09-2023 03:51:32  Hopper-v4  25000    6.488228  COMPER-DDPG"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path ='COMPER_DDPG/data_using_done/'+env+'/eval/progress.json'\n",
    "df_comper_eval = pd.read_json(path,lines=True)\n",
    "df_comper_eval[\"Method\"]=\"COMPER-DDPG\"\n",
    "print(df_comper_eval.shape)\n",
    "df_comper_eval.head()"
   ]
  },
  {
   "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>Trial</th>\n",
       "      <th>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>6.370542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>14507.211437</td>\n",
       "      <td>0.368101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>4.636773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>6.411029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>6.440084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>6.486812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>6.643710</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Trial           Int  EvalAvgRew\n",
       "count  50.000000     50.000000   50.000000\n",
       "mean    3.000000  27500.000000    6.370542\n",
       "std     1.428571  14507.211437    0.368101\n",
       "min     1.000000   5000.000000    4.636773\n",
       "25%     2.000000  15000.000000    6.411029\n",
       "50%     3.000000  27500.000000    6.440084\n",
       "75%     4.000000  40000.000000    6.486812\n",
       "max     5.000000  50000.000000    6.643710"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comper_eval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(50, 6)\n"
     ]
    },
    {
     "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>Trial</th>\n",
       "      <th>Time</th>\n",
       "      <th>Task</th>\n",
       "      <th>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "      <th>Method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>04-10-2023 04:20:26</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>5000</td>\n",
       "      <td>6.394962</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>04-10-2023 04:30:41</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>10000</td>\n",
       "      <td>6.494615</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>04-10-2023 04:41:33</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>15000</td>\n",
       "      <td>6.483611</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>04-10-2023 04:53:02</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>20000</td>\n",
       "      <td>6.163105</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>04-10-2023 05:05:04</td>\n",
       "      <td>Hopper-v4</td>\n",
       "      <td>25000</td>\n",
       "      <td>6.388219</td>\n",
       "      <td>DDPG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Trial                 Time       Task    Int  EvalAvgRew Method\n",
       "0      1  04-10-2023 04:20:26  Hopper-v4   5000    6.394962   DDPG\n",
       "1      1  04-10-2023 04:30:41  Hopper-v4  10000    6.494615   DDPG\n",
       "2      1  04-10-2023 04:41:33  Hopper-v4  15000    6.483611   DDPG\n",
       "3      1  04-10-2023 04:53:02  Hopper-v4  20000    6.163105   DDPG\n",
       "4      1  04-10-2023 05:05:04  Hopper-v4  25000    6.388219   DDPG"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path ='DPPG/data_using_done/'+env+'/eval/progress.json'\n",
    "df_ddpg_eval = pd.read_json(path,lines=True)\n",
    "df_ddpg_eval[\"Method\"]=\"DDPG\"\n",
    "print(df_ddpg_eval.shape)\n",
    "df_ddpg_eval.head()"
   ]
  },
  {
   "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>Trial</th>\n",
       "      <th>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>5.062366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.428571</td>\n",
       "      <td>14507.211437</td>\n",
       "      <td>1.494747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>2.289988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>3.752267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>5.581239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>40000.000000</td>\n",
       "      <td>6.376379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>50000.000000</td>\n",
       "      <td>6.531706</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Trial           Int  EvalAvgRew\n",
       "count  50.000000     50.000000   50.000000\n",
       "mean    3.000000  27500.000000    5.062366\n",
       "std     1.428571  14507.211437    1.494747\n",
       "min     1.000000   5000.000000    2.289988\n",
       "25%     2.000000  15000.000000    3.752267\n",
       "50%     3.000000  27500.000000    5.581239\n",
       "75%     4.000000  40000.000000    6.376379\n",
       "max     5.000000  50000.000000    6.531706"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ddpg_eval.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_eval=pd.concat([df_comper_eval,df_ddpg_eval])\n",
    "df_eval.rename(columns = {'Int':'Itr'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Itr', ylabel='EvalAvgRew'>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df_eval, x=\"Itr\", y=\"EvalAvgRew\",hue='Method')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## USING ROLLING AVERAGE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>TotalItr</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1377</td>\n",
       "      <td>40.201005</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>18.384031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1399</td>\n",
       "      <td>39.028051</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>17.907506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1400</td>\n",
       "      <td>1.016158</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.863805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1422</td>\n",
       "      <td>40.316259</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.370839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1445</td>\n",
       "      <td>40.049428</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.217931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalItr      EpRew       Method  EpRewRolling\n",
       "99       1377  40.201005  COMPER-DDPG     18.384031\n",
       "100      1399  39.028051  COMPER-DDPG     17.907506\n",
       "101      1400   1.016158  COMPER-DDPG     16.863805\n",
       "102      1422  40.316259  COMPER-DDPG     16.370839\n",
       "103      1445  40.049428  COMPER-DDPG     16.217931"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comper_rolling_avg = pd.DataFrame(df_comper[[\"TotalItr\",\"EpRew\",\"Method\"]])\n",
    "df_comper_rolling_avg[\"EpRewRolling\"]=df_comper_rolling_avg['EpRew'].rolling(100).mean()\n",
    "df_comper_rolling_avg.dropna(inplace=True)\n",
    "df_comper_rolling_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>TotalItr</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2430</td>\n",
       "      <td>65.551900</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>39.696073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2506</td>\n",
       "      <td>145.321659</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>40.285726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>2531</td>\n",
       "      <td>44.272141</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>39.914693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>2562</td>\n",
       "      <td>55.541601</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>40.433212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>2584</td>\n",
       "      <td>38.611536</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>40.782276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalItr       EpRew Method  EpRewRolling\n",
       "99       2430   65.551900   DDPG     39.696073\n",
       "100      2506  145.321659   DDPG     40.285726\n",
       "101      2531   44.272141   DDPG     39.914693\n",
       "102      2562   55.541601   DDPG     40.433212\n",
       "103      2584   38.611536   DDPG     40.782276"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ddpg_rolling_avg=pd.DataFrame(df_ddpg[[\"TotalItr\",\"EpRew\",\"Method\"]])\n",
    "df_ddpg_rolling_avg[\"EpRewRolling\"]=df_ddpg_rolling_avg[\"EpRew\"].rolling(100).mean()\n",
    "df_ddpg_rolling_avg.dropna(inplace=True)\n",
    "df_ddpg_rolling_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>TotalItr</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1377</td>\n",
       "      <td>40.201005</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>18.384031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>1399</td>\n",
       "      <td>39.028051</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>17.907506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>1400</td>\n",
       "      <td>1.016158</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.863805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1422</td>\n",
       "      <td>40.316259</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.370839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>1445</td>\n",
       "      <td>40.049428</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>16.217931</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TotalItr      EpRew       Method  EpRewRolling\n",
       "99       1377  40.201005  COMPER-DDPG     18.384031\n",
       "100      1399  39.028051  COMPER-DDPG     17.907506\n",
       "101      1400   1.016158  COMPER-DDPG     16.863805\n",
       "102      1422  40.316259  COMPER-DDPG     16.370839\n",
       "103      1445  40.049428  COMPER-DDPG     16.217931"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolling=pd.concat([df_comper_rolling_avg,df_ddpg_rolling_avg])\n",
    "df_rolling.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>TotalItr</th>\n",
       "      <th>EpRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4676</th>\n",
       "      <td>49978</td>\n",
       "      <td>4.188887</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>4.655494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4677</th>\n",
       "      <td>49985</td>\n",
       "      <td>4.219903</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>4.640064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4678</th>\n",
       "      <td>49992</td>\n",
       "      <td>4.254085</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>4.613485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4679</th>\n",
       "      <td>49999</td>\n",
       "      <td>4.300753</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>4.584645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4680</th>\n",
       "      <td>50000</td>\n",
       "      <td>0.946108</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>4.520955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TotalItr     EpRew Method  EpRewRolling\n",
       "4676     49978  4.188887   DDPG      4.655494\n",
       "4677     49985  4.219903   DDPG      4.640064\n",
       "4678     49992  4.254085   DDPG      4.613485\n",
       "4679     49999  4.300753   DDPG      4.584645\n",
       "4680     50000  0.946108   DDPG      4.520955"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rolling.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='TotalItr', ylabel='EpRewRolling'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.Figure()\n",
    "fig.set_figwidth(15)\n",
    "fig.set_figheight(10)\n",
    "sns.lineplot(data=df_rolling, x=\"TotalItr\", y=\"EpRewRolling\",hue='Method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000</td>\n",
       "      <td>6.441560</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>6.463934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000</td>\n",
       "      <td>6.431958</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>6.436759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000</td>\n",
       "      <td>6.497798</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>6.464878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25000</td>\n",
       "      <td>6.488228</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>6.493013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30000</td>\n",
       "      <td>6.435348</td>\n",
       "      <td>COMPER-DDPG</td>\n",
       "      <td>6.461788</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Int  EvalAvgRew       Method  EpRewRolling\n",
       "1  10000    6.441560  COMPER-DDPG      6.463934\n",
       "2  15000    6.431958  COMPER-DDPG      6.436759\n",
       "3  20000    6.497798  COMPER-DDPG      6.464878\n",
       "4  25000    6.488228  COMPER-DDPG      6.493013\n",
       "5  30000    6.435348  COMPER-DDPG      6.461788"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comper_eval_rolling_avg = pd.DataFrame(df_comper_eval[[\"Int\",\"EvalAvgRew\",\"Method\"]])\n",
    "df_comper_eval_rolling_avg[\"EpRewRolling\"]=df_comper_eval[\"EvalAvgRew\"].rolling(2).mean()\n",
    "df_comper_eval_rolling_avg.dropna(inplace=True)\n",
    "df_comper_eval_rolling_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Int</th>\n",
       "      <th>EvalAvgRew</th>\n",
       "      <th>Method</th>\n",
       "      <th>EpRewRolling</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10000</td>\n",
       "      <td>6.494615</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>6.444789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15000</td>\n",
       "      <td>6.483611</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>6.489113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20000</td>\n",
       "      <td>6.163105</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>6.323358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25000</td>\n",
       "      <td>6.388219</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>6.275662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30000</td>\n",
       "      <td>4.282818</td>\n",
       "      <td>DDPG</td>\n",
       "      <td>5.335518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Int  EvalAvgRew Method  EpRewRolling\n",
       "1  10000    6.494615   DDPG      6.444789\n",
       "2  15000    6.483611   DDPG      6.489113\n",
       "3  20000    6.163105   DDPG      6.323358\n",
       "4  25000    6.388219   DDPG      6.275662\n",
       "5  30000    4.282818   DDPG      5.335518"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ddpg_eval_rolling_avg = pd.DataFrame(df_ddpg_eval[[\"Int\",\"EvalAvgRew\",\"Method\"]])\n",
    "df_ddpg_eval_rolling_avg[\"EpRewRolling\"]=df_ddpg_eval[\"EvalAvgRew\"].rolling(2).mean()\n",
    "df_ddpg_eval_rolling_avg.dropna(inplace=True)\n",
    "df_ddpg_eval_rolling_avg.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Int', ylabel='EpRewRolling'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_eval_rolling=pd.concat([df_comper_eval_rolling_avg,df_ddpg_eval_rolling_avg])\n",
    "sns.lineplot(data=df_eval_rolling, x=\"Int\", y=\"EpRewRolling\",hue='Method')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x7f66cbc26310>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(data=df, kind=\"bar\",x=\"Method\", y=\"Avg100Ep\", hue=\"Method\",\n",
    "    errorbar=\"sd\")\n",
    "g.despine(left=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 654.75x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(\n",
    "    data=df,\n",
    "    x=\"TotalItr\", y=\"Avg100Ep\", hue=\"Method\",\n",
    "    height=5\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "results_analisy",
   "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": "3.1.0"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}