ap_rllib_experiment_analysis/notebooks/youshallnotpass_rllib-analysis-external-recover-withnormal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ray.tune.analysis import Analysis\n",
    "from matplotlib import pyplot as plt\n",
    "import plotly.express as px\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "analysis = Analysis(\"/home/sergei/ray_results/adversarial_tune_recover_withnormal_sb\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'policy_reward_mean/player_2_pretrained_sb'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = analysis.dataframe(metric=target, mode='max')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "episode_reward_max                                                     7.04425\n",
       "episode_reward_min                                                    -2.53259\n",
       "episode_reward_mean                                                    4.48875\n",
       "episode_len_mean                                                       173.381\n",
       "episodes_this_iter                                                         113\n",
       "                                                   ...                        \n",
       "config/run_uid                                                          _setme\n",
       "config/sgd_minibatch_size                                                 4096\n",
       "config/train_batch_size                                                  16384\n",
       "config/use_gae                                                            True\n",
       "logdir                       /home/sergei/ray_results/adversarial_tune_reco...\n",
       "Name: 0, Length: 102, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['episode_reward_max', 'episode_reward_min', 'episode_reward_mean',\n",
       "       'episode_len_mean', 'episodes_this_iter', 'num_healthy_workers',\n",
       "       'timesteps_total', 'done', 'episodes_total', 'training_iteration',\n",
       "       ...\n",
       "       'config/num_envs_per_worker', 'config/num_gpus', 'config/num_sgd_iter',\n",
       "       'config/num_workers', 'config/rollout_fragment_length',\n",
       "       'config/run_uid', 'config/sgd_minibatch_size',\n",
       "       'config/train_batch_size', 'config/use_gae', 'logdir'],\n",
       "      dtype='object', length=102)"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "for k in df.columns:\n",
    "#     if k.startswith('episode'):\n",
    "#         del df[k]\n",
    "    if not (k.startswith('config') or k == target or k == 'checkpoint_rllib' or k == 'logdir' or k == 'experiment_tag'):\n",
    "        del df[k]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sort_values(target, ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>checkpoint_rllib</th>\n",
       "      <th>experiment_tag</th>\n",
       "      <th>policy_reward_mean/player_2_pretrained_sb</th>\n",
       "      <th>config/_base_dir</th>\n",
       "      <th>config/_call</th>\n",
       "      <th>config/_checkpoint_restore_policy</th>\n",
       "      <th>config/_env</th>\n",
       "      <th>config/_env_fcn</th>\n",
       "      <th>config/_env_name_rllib</th>\n",
       "      <th>config/_get_policies</th>\n",
       "      <th>...</th>\n",
       "      <th>config/num_envs_per_worker</th>\n",
       "      <th>config/num_gpus</th>\n",
       "      <th>config/num_sgd_iter</th>\n",
       "      <th>config/num_workers</th>\n",
       "      <th>config/rollout_fragment_length</th>\n",
       "      <th>config/run_uid</th>\n",
       "      <th>config/sgd_minibatch_size</th>\n",
       "      <th>config/train_batch_size</th>\n",
       "      <th>config/use_gae</th>\n",
       "      <th>logdir</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/sergei/ray_results/External_multicomp_20...</td>\n",
       "      <td>4</td>\n",
       "      <td>12.091342</td>\n",
       "      <td>/scratch/sergei/better-adversarial-defenses</td>\n",
       "      <td>{'checkpoint_freq': 0, 'name': 'adversarial_tu...</td>\n",
       "      <td>{'player_1_pretrained_adversary_sb': './result...</td>\n",
       "      <td>{'SingleAgentToMultiAgent': False, 'env_name':...</td>\n",
       "      <td>&lt;function create_env at 0x7f8686657170&gt;</td>\n",
       "      <td>multicomp</td>\n",
       "      <td>&lt;function get_policies_withnormal_sb at 0x7f86...</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>_setme</td>\n",
       "      <td>4096</td>\n",
       "      <td>16384</td>\n",
       "      <td>True</td>\n",
       "      <td>/home/sergei/ray_results/adversarial_tune_reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/sergei/ray_results/External_multicomp_20...</td>\n",
       "      <td>3</td>\n",
       "      <td>11.467525</td>\n",
       "      <td>/scratch/sergei/better-adversarial-defenses</td>\n",
       "      <td>{'checkpoint_freq': 0, 'name': 'adversarial_tu...</td>\n",
       "      <td>{'player_1_pretrained_adversary_sb': './result...</td>\n",
       "      <td>{'SingleAgentToMultiAgent': False, 'env_name':...</td>\n",
       "      <td>&lt;function create_env at 0x7f8686657170&gt;</td>\n",
       "      <td>multicomp</td>\n",
       "      <td>&lt;function get_policies_withnormal_sb at 0x7f86...</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>_setme</td>\n",
       "      <td>4096</td>\n",
       "      <td>16384</td>\n",
       "      <td>True</td>\n",
       "      <td>/home/sergei/ray_results/adversarial_tune_reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/sergei/ray_results/External_multicomp_20...</td>\n",
       "      <td>0</td>\n",
       "      <td>11.366417</td>\n",
       "      <td>/scratch/sergei/better-adversarial-defenses</td>\n",
       "      <td>{'checkpoint_freq': 0, 'name': 'adversarial_tu...</td>\n",
       "      <td>{'player_1_pretrained_adversary_sb': './result...</td>\n",
       "      <td>{'SingleAgentToMultiAgent': False, 'env_name':...</td>\n",
       "      <td>&lt;function create_env at 0x7f8686657170&gt;</td>\n",
       "      <td>multicomp</td>\n",
       "      <td>&lt;function get_policies_withnormal_sb at 0x7f86...</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>_setme</td>\n",
       "      <td>4096</td>\n",
       "      <td>16384</td>\n",
       "      <td>True</td>\n",
       "      <td>/home/sergei/ray_results/adversarial_tune_reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/sergei/ray_results/External_multicomp_20...</td>\n",
       "      <td>2</td>\n",
       "      <td>11.302904</td>\n",
       "      <td>/scratch/sergei/better-adversarial-defenses</td>\n",
       "      <td>{'checkpoint_freq': 0, 'name': 'adversarial_tu...</td>\n",
       "      <td>{'player_1_pretrained_adversary_sb': './result...</td>\n",
       "      <td>{'SingleAgentToMultiAgent': False, 'env_name':...</td>\n",
       "      <td>&lt;function create_env at 0x7f8686657170&gt;</td>\n",
       "      <td>multicomp</td>\n",
       "      <td>&lt;function get_policies_withnormal_sb at 0x7f86...</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>_setme</td>\n",
       "      <td>4096</td>\n",
       "      <td>16384</td>\n",
       "      <td>True</td>\n",
       "      <td>/home/sergei/ray_results/adversarial_tune_reco...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/sergei/ray_results/External_multicomp_20...</td>\n",
       "      <td>1</td>\n",
       "      <td>10.933282</td>\n",
       "      <td>/scratch/sergei/better-adversarial-defenses</td>\n",
       "      <td>{'checkpoint_freq': 0, 'name': 'adversarial_tu...</td>\n",
       "      <td>{'player_1_pretrained_adversary_sb': './result...</td>\n",
       "      <td>{'SingleAgentToMultiAgent': False, 'env_name':...</td>\n",
       "      <td>&lt;function create_env at 0x7f8686657170&gt;</td>\n",
       "      <td>multicomp</td>\n",
       "      <td>&lt;function get_policies_withnormal_sb at 0x7f86...</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>_setme</td>\n",
       "      <td>4096</td>\n",
       "      <td>16384</td>\n",
       "      <td>True</td>\n",
       "      <td>/home/sergei/ray_results/adversarial_tune_reco...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    checkpoint_rllib  experiment_tag  \\\n",
       "1  /home/sergei/ray_results/External_multicomp_20...               4   \n",
       "3  /home/sergei/ray_results/External_multicomp_20...               3   \n",
       "4  /home/sergei/ray_results/External_multicomp_20...               0   \n",
       "0  /home/sergei/ray_results/External_multicomp_20...               2   \n",
       "2  /home/sergei/ray_results/External_multicomp_20...               1   \n",
       "\n",
       "   policy_reward_mean/player_2_pretrained_sb  \\\n",
       "1                                  12.091342   \n",
       "3                                  11.467525   \n",
       "4                                  11.366417   \n",
       "0                                  11.302904   \n",
       "2                                  10.933282   \n",
       "\n",
       "                              config/_base_dir  \\\n",
       "1  /scratch/sergei/better-adversarial-defenses   \n",
       "3  /scratch/sergei/better-adversarial-defenses   \n",
       "4  /scratch/sergei/better-adversarial-defenses   \n",
       "0  /scratch/sergei/better-adversarial-defenses   \n",
       "2  /scratch/sergei/better-adversarial-defenses   \n",
       "\n",
       "                                        config/_call  \\\n",
       "1  {'checkpoint_freq': 0, 'name': 'adversarial_tu...   \n",
       "3  {'checkpoint_freq': 0, 'name': 'adversarial_tu...   \n",
       "4  {'checkpoint_freq': 0, 'name': 'adversarial_tu...   \n",
       "0  {'checkpoint_freq': 0, 'name': 'adversarial_tu...   \n",
       "2  {'checkpoint_freq': 0, 'name': 'adversarial_tu...   \n",
       "\n",
       "                   config/_checkpoint_restore_policy  \\\n",
       "1  {'player_1_pretrained_adversary_sb': './result...   \n",
       "3  {'player_1_pretrained_adversary_sb': './result...   \n",
       "4  {'player_1_pretrained_adversary_sb': './result...   \n",
       "0  {'player_1_pretrained_adversary_sb': './result...   \n",
       "2  {'player_1_pretrained_adversary_sb': './result...   \n",
       "\n",
       "                                         config/_env  \\\n",
       "1  {'SingleAgentToMultiAgent': False, 'env_name':...   \n",
       "3  {'SingleAgentToMultiAgent': False, 'env_name':...   \n",
       "4  {'SingleAgentToMultiAgent': False, 'env_name':...   \n",
       "0  {'SingleAgentToMultiAgent': False, 'env_name':...   \n",
       "2  {'SingleAgentToMultiAgent': False, 'env_name':...   \n",
       "\n",
       "                           config/_env_fcn config/_env_name_rllib  \\\n",
       "1  <function create_env at 0x7f8686657170>              multicomp   \n",
       "3  <function create_env at 0x7f8686657170>              multicomp   \n",
       "4  <function create_env at 0x7f8686657170>              multicomp   \n",
       "0  <function create_env at 0x7f8686657170>              multicomp   \n",
       "2  <function create_env at 0x7f8686657170>              multicomp   \n",
       "\n",
       "                                config/_get_policies  ...  \\\n",
       "1  <function get_policies_withnormal_sb at 0x7f86...  ...   \n",
       "3  <function get_policies_withnormal_sb at 0x7f86...  ...   \n",
       "4  <function get_policies_withnormal_sb at 0x7f86...  ...   \n",
       "0  <function get_policies_withnormal_sb at 0x7f86...  ...   \n",
       "2  <function get_policies_withnormal_sb at 0x7f86...  ...   \n",
       "\n",
       "   config/num_envs_per_worker config/num_gpus config/num_sgd_iter  \\\n",
       "1                          10               0                   4   \n",
       "3                          10               0                   4   \n",
       "4                          10               0                   4   \n",
       "0                          10               0                   4   \n",
       "2                          10               0                   4   \n",
       "\n",
       "   config/num_workers  config/rollout_fragment_length config/run_uid  \\\n",
       "1                   3                             100         _setme   \n",
       "3                   3                             100         _setme   \n",
       "4                   3                             100         _setme   \n",
       "0                   3                             100         _setme   \n",
       "2                   3                             100         _setme   \n",
       "\n",
       "  config/sgd_minibatch_size config/train_batch_size  config/use_gae  \\\n",
       "1                      4096                   16384            True   \n",
       "3                      4096                   16384            True   \n",
       "4                      4096                   16384            True   \n",
       "0                      4096                   16384            True   \n",
       "2                      4096                   16384            True   \n",
       "\n",
       "                                              logdir  \n",
       "1  /home/sergei/ray_results/adversarial_tune_reco...  \n",
       "3  /home/sergei/ray_results/adversarial_tune_reco...  \n",
       "4  /home/sergei/ray_results/adversarial_tune_reco...  \n",
       "0  /home/sergei/ray_results/adversarial_tune_reco...  \n",
       "2  /home/sergei/ray_results/adversarial_tune_reco...  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1., 0., 0., 2., 1., 0., 0., 0., 0., 1.]),\n",
       " array([10.9332825 , 11.04908847, 11.16489445, 11.28070042, 11.3965064 ,\n",
       "        11.51231237, 11.62811835, 11.74392432, 11.8597303 , 11.97553627,\n",
       "        12.09134225]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df[target])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "checkpoint_rllib                             /home/sergei/ray_results/External_multicomp_20...\n",
       "experiment_tag                                                                               3\n",
       "policy_reward_mean/player_2_pretrained_sb                                              11.4675\n",
       "config/_base_dir                                   /scratch/sergei/better-adversarial-defenses\n",
       "config/_call                                 {'checkpoint_freq': 0, 'name': 'adversarial_tu...\n",
       "config/_checkpoint_restore_policy            {'player_1_pretrained_adversary_sb': './result...\n",
       "config/_env                                  {'SingleAgentToMultiAgent': False, 'env_name':...\n",
       "config/_env_fcn                                        <function create_env at 0x7f8686657170>\n",
       "config/_env_name_rllib                                                               multicomp\n",
       "config/_get_policies                         <function get_policies_withnormal_sb at 0x7f86...\n",
       "config/_log_error                                                                         True\n",
       "config/_main_filename                        /scratch/sergei/better-adversarial-defenses/tr...\n",
       "config/_model_params                         {'fcnet_activation': 'tanh', 'fcnet_hiddens': ...\n",
       "config/_num_workers_tf                                                                       4\n",
       "config/_p_normal                                                                           0.5\n",
       "config/_policies                                              [None, from_scratch, pretrained]\n",
       "config/_policy                                                                             PPO\n",
       "config/_redis_address                                                        128.32.175.9:6379\n",
       "config/_run_inline                                                                       False\n",
       "config/_select_policy                        <function select_policy_opp_normal_and_adv_sb ...\n",
       "config/_tmp_dir                                                                           /tmp\n",
       "config/_train_policies                                                [player_2_pretrained_sb]\n",
       "config/_train_steps                                                                 9999999999\n",
       "config/_trainer                                                                       External\n",
       "config/_update_config                                                                     None\n",
       "config/_verbose                                                                          False\n",
       "config/batch_mode                                                            complete_episodes\n",
       "config/framework                                                                           tfe\n",
       "config/http_remote_port                                                 http://127.0.0.1:50001\n",
       "config/kl_coeff                                                                              1\n",
       "config/lr                                                                               0.0003\n",
       "config/num_envs_per_worker                                                                  10\n",
       "config/num_gpus                                                                              0\n",
       "config/num_sgd_iter                                                                          4\n",
       "config/num_workers                                                                           3\n",
       "config/rollout_fragment_length                                                             100\n",
       "config/run_uid                                                                          _setme\n",
       "config/sgd_minibatch_size                                                                 4096\n",
       "config/train_batch_size                                                                  16384\n",
       "config/use_gae                                                                            True\n",
       "logdir                                       /home/sergei/ray_results/adversarial_tune_reco...\n",
       "Name: 3, dtype: object"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 1\n",
    "best_trial = df.iloc[idx].logdir\n",
    "df.iloc[idx]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for k, df_ in analysis.trial_dataframes.items():\n",
    "    rew = df_[target]\n",
    "    plt.plot(pd.Series(rew).rolling(10).mean()[:4000], label=k.split('-')[-1])\n",
    "    #imax = np.argmax(rew)\n",
    "    plt.legend()\n",
    "#     imax = -1\n",
    "#     plt.axvline(imax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/sergei/ray_results/adversarial_tune_recover_withnormal_sb/train_one_with_sacred_4_2020-09-21_11-36-44hhwlmt9u'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "idx = 0\n",
    "best_trial = df.iloc[idx].logdir\n",
    "df.iloc[idx]\n",
    "best_trial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_best = analysis.trial_dataframes[best_trial]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/sergei/ray_results/External_multicomp_2020-09-22_10-55-23_joq3ccx/checkpoint_1907/checkpoint-1907'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "checkpoint = df_best.checkpoint_rllib.iloc[imax]\n",
    "checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n",
      "Config {'env': 'multicomp', 'env_config': {'with_video': False, 'SingleAgentToMultiAgent': False, 'env_name': 'multicomp/YouShallNotPassHumans-v0'}, 'multiagent': {'policies_to_train': [], 'policies': {'player_1_pretrained_adversary_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': 'normalization_only'}}, 'framework': 'tfe'}), 'player_1_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': True}}, 'framework': 'tfe'}), 'player_2_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 1, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_1', 'load_weights': True}}, 'framework': 'tfe'})}, 'policy_mapping_fn': functools.partial(<function select_policy_opp_normal_and_adv_sb at 0x7fc410a68680>, config={'kl_coeff': 1.0, '_num_workers_tf': 4, 'use_gae': True, 'num_gpus': 0, '_env_name_rllib': 'multicomp', '_env_fcn': <function create_env at 0x7fc410d97950>, '_policies': [None, 'from_scratch', 'pretrained'], '_env': {'with_video': False, 'SingleAgentToMultiAgent': False, 'env_name': 'multicomp/YouShallNotPassHumans-v0'}, 'framework': 'tfe', '_train_policies': [], '_call': {'checkpoint_freq': 0, 'name': 'adversarial_tune_recover_withnormal_sb', 'stop': {'timesteps_total': 50000000}, 'num_samples': 5, 'resources_per_trial': {'custom_resources': {'tune_cpu': 3}}}, '_trainer': 'External', '_policy': 'PPO', '_train_steps': 50, '_update_config': None, '_run_inline': False, 'num_envs_per_worker': 1, '_log_error': True, '_model_params': {'use_lstm': False, 'fcnet_hiddens': [64, 64], 'fcnet_activation': 'tanh', 'free_log_std': True}, '_select_policy': <function select_policy_opp_normal_and_adv_sb at 0x7fc410a68680>, '_get_policies': <function get_policies_withnormal_sb at 0x7fc410a685f0>, '_checkpoint_restore_policy': {'player_1_pretrained_adversary_sb': './results/checkpoint-adv-external-3273-player_1.pkl'}, 'batch_mode': 'complete_episodes', 'train_batch_size': 256, 'lr': 0, 'sgd_minibatch_size': 256, 'num_sgd_iter': 1, 'rollout_fragment_length': 200, 'run_uid': '_setme', 'http_remote_port': 'http://127.0.0.1:50001', 'num_workers': 0, '_p_normal': 0.5, '_verbose': False, 'multiagent': {'policies': {'player_1_pretrained_adversary_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': 'normalization_only'}}, 'framework': 'tfe'}), 'player_1_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': True}}, 'framework': 'tfe'}), 'player_2_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 1, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_1', 'load_weights': True}}, 'framework': 'tfe'})}}})}, 'tf_session_args': {'intra_op_parallelism_threads': 4, 'inter_op_parallelism_threads': 4, 'gpu_options': {'allow_growth': True}, 'log_device_placement': True, 'device_count': {'CPU': 4}, 'allow_soft_placement': True}, 'local_tf_session_args': {'intra_op_parallelism_threads': 4, 'inter_op_parallelism_threads': 4}, 'kl_coeff': 1.0, 'use_gae': True, 'num_gpus': 0, 'framework': 'tfe', 'num_envs_per_worker': 1, 'batch_mode': 'complete_episodes', 'train_batch_size': 256, 'lr': 0, 'sgd_minibatch_size': 256, 'num_sgd_iter': 1, 'rollout_fragment_length': 200, 'run_uid': '_setme', 'http_remote_port': 'http://127.0.0.1:50001', 'num_workers': 0}\n",
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n",
      "Not setting NN weights\n",
      "Loading normalization\n",
      "WARNING:tensorflow:From /scratch/sergei/miniconda3/lib/python3.7/site-packages/ray/rllib/models/tf/tf_action_dist.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "Setting NN weights\n",
      "Loading normalization\n",
      "Setting NN weights\n",
      "Loading normalization\n",
      "  0%|                                                    | 0/50 [00:00<?, ?it/s]WARNING:tensorflow:From /scratch/sergei/miniconda3/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:761: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Prefer Variable.assign which has equivalent behavior in 2.X.\n",
      "100%|███████████████████████████████████████████| 50/50 [03:24<00:00,  4.08s/it]\n",
      "{'episode_reward': [-2.4647814364434453, 6.395752121636178, 1.9345218170492444, 6.623052882408956, 0.7336647043484845, 5.895737726677908, 6.2352421383111505, -0.4557078882062342, -1.3243224909820128, 5.9674520241242135, -1.6826776681118645, -0.6870052273807232, -1.4725595302697911, -3.832074305297283, 6.112105107829848, 6.5138092979905196, 0.011013974552042782, 6.294096065306803, 6.054417879640823, 5.852010114937002, -5.552111949014943, 3.100236278347438, 6.2251192552653265, 6.275308165320894, -2.1030257000529673, 6.173239299943816, 6.363030081360193, 6.350148179917596, -1.6977907728960417, 6.309625673606206, 6.0427074581966735, 6.2593946866254555, -0.44635704180109315, 6.294625820744841, -2.6728599699563347, 6.010876928921789, 3.430196219618665, 6.517348429075355, -0.6402889112941921, 6.120439616620388, 6.641834901034599, 1.0000827024996397, -0.26895633645472117, 5.9944161808962235, 7.333749214296404, 5.311077096659574, 6.100494910569978, 6.928607600129908, 7.71311742378748, -0.7512576499138959, 6.225881121354178, 5.9647296715002085, -1.874770880138385, 6.13718118192628, 3.0535900627969568, 4.49312405474484, 4.015645686798962, 6.2455352101969765, 3.923222706944216, -0.6212786555406637, -0.9441175014944747, -0.6508016612497158, 0.376678532709775, -0.50784505638876, 5.836741622726549, -12.881673562776996, 6.346959155598597, 6.142697250683796, 4.426044112431555, 4.405135280317836, 0.08674981258809566, 8.221175705490168, 6.1801533699035645, 6.728429744791356, 6.706869945432118, 1.1871701891723205, 1.0142597214689886, -0.7948790816810742, 6.295009584758191, -0.7294743505481165, 6.395340764262073, 6.269142002201988, 2.496324058258324, -0.6187793581630103, 6.9395114912767895, 6.481912881950848, 3.4835572651209077, 6.344393831139314, 6.098489998898003, 6.113490431314858, 4.891461297760543, 6.324789351576328, 6.169482543201866, -0.6283597751535126, -1.118941238426487, 6.6536841710912995, -3.471432230377104, 6.240603110658412, 6.698558011205023, -0.22230890503851697], 'policy_player_1_pretrained_sb_reward': [10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, -10.0, 10.0, -10.0, 10.0, -10.0, -10.0, 10.0, 10.0, -10.0, -10.0, 10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, 10.0, 10.0, -10.0, -10.0, -10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0, -10.0, -10.0, 10.0, 10.0, 10.0, -10.0, -10.0, -10.0, -10.0, 10.0, 10.0, -10.0, -10.0], 'policy_player_2_pretrained_sb_reward': [-12.464781436443445, 16.39575212163618, -8.065478182950756, 16.623052882408956, 10.733664704348485, 15.895737726677908, 16.23524213831115, -10.455707888206234, -11.324322490982013, 15.967452024124213, -11.682677668111864, -10.687005227380723, -11.472559530269791, -13.832074305297283, 16.112105107829848, 16.51380929799052, -9.988986025447957, 16.294096065306803, 16.054417879640823, 15.852010114937002, -15.552111949014943, -6.899763721652562, 16.225119255265327, 16.275308165320894, -12.103025700052967, 16.173239299943816, 16.363030081360193, 16.350148179917596, -11.697790772896042, 16.309625673606206, 16.042707458196674, 16.259394686625456, -10.446357041801093, 16.29462582074484, 7.327140030043665, 16.01087692892179, -6.569803780381335, 16.517348429075355, -10.640288911294192, 16.120439616620388, 16.6418349010346, -8.99991729750036, -10.268956336454721, 15.994416180896224, 17.333749214296404, 15.311077096659574, 16.100494910569978, 16.928607600129908, 17.71311742378748, -10.751257649913896, 16.225881121354178, 15.964729671500208, -11.874770880138385, 16.13718118192628, 13.053590062796957, 14.49312405474484, 14.015645686798962, 16.245535210196977, 13.923222706944216, -10.621278655540664, -10.944117501494475, -10.650801661249716, -9.623321467290225, -10.50784505638876, 15.83674162272655, -22.881673562776996, 16.346959155598597, 16.142697250683796, 14.426044112431555, 14.405135280317836, -9.913250187411904, 18.221175705490168, 16.180153369903564, 16.728429744791356, 16.706869945432118, -8.81282981082768, -8.985740278531011, -10.794879081681074, 16.29500958475819, 9.270525649451884, 16.395340764262073, 16.26914200220199, 12.496324058258324, -10.61877935816301, 16.93951149127679, 16.481912881950848, -6.516442734879092, 16.344393831139314, 16.098489998898003, 16.113490431314858, 14.891461297760543, 16.324789351576328, 16.169482543201866, -10.628359775153513, -11.118941238426487, 16.6536841710913, 6.528567769622896, 16.240603110658412, 16.698558011205023, 9.777691094961483], 'policy_player_1_pretrained_adversary_sb_reward': [-10.0, -10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, 10.0, -10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, 10.0, -10.0, 10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0]}\n",
      "Player episode_reward total trials 100 win rate 72.0% loss rate 28.0% tie rate 0.0%\n",
      "Player policy_player_1_pretrained_sb_reward total trials 49 win rate 55.10204081632653% loss rate 44.89795918367347% tie rate 0.0%\n",
      "Player policy_player_2_pretrained_sb_reward total trials 100 win rate 66.0% loss rate 34.0% tie rate 0.0%\n",
      "Player policy_player_1_pretrained_adversary_sb_reward total trials 51 win rate 13.72549019607843% loss rate 86.27450980392157% tie rate 0.0%\n"
     ]
    }
   ],
   "source": [
    "!python ../make_video.py --checkpoint {checkpoint} --no_video=True --steps 50 --config=victim_recover_withnormal_sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n",
      "GLFW error: 65544, desc: b'X11: RandR gamma ramp support seems broken'\n",
      "Config {'env': 'multicomp', 'env_config': {'with_video': True, 'SingleAgentToMultiAgent': False, 'env_name': 'multicomp/YouShallNotPassHumans-v0'}, 'multiagent': {'policies_to_train': [], 'policies': {'player_1_pretrained_adversary_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': 'normalization_only'}}, 'framework': 'tfe'}), 'player_1_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': True}}, 'framework': 'tfe'}), 'player_2_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 1, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_1', 'load_weights': True}}, 'framework': 'tfe'})}, 'policy_mapping_fn': functools.partial(<function select_policy_opp_normal_and_adv_sb at 0x7f4bb6ac4710>, config={'kl_coeff': 1.0, '_num_workers_tf': 4, 'use_gae': True, 'num_gpus': 0, '_env_name_rllib': 'multicomp', '_env_fcn': <function create_env at 0x7f4bb6df29e0>, '_policies': [None, 'from_scratch', 'pretrained'], '_env': {'with_video': True, 'SingleAgentToMultiAgent': False, 'env_name': 'multicomp/YouShallNotPassHumans-v0'}, 'framework': 'tfe', '_train_policies': [], '_call': {'checkpoint_freq': 0, 'name': 'adversarial_tune_recover_withnormal_sb', 'stop': {'timesteps_total': 50000000}, 'num_samples': 5, 'resources_per_trial': {'custom_resources': {'tune_cpu': 3}}}, '_trainer': 'External', '_policy': 'PPO', '_train_steps': 10, '_update_config': None, '_run_inline': False, 'num_envs_per_worker': 1, '_log_error': True, '_model_params': {'use_lstm': False, 'fcnet_hiddens': [64, 64], 'fcnet_activation': 'tanh', 'free_log_std': True}, '_select_policy': <function select_policy_opp_normal_and_adv_sb at 0x7f4bb6ac4710>, '_get_policies': <function get_policies_withnormal_sb at 0x7f4bb6ac4680>, '_checkpoint_restore_policy': {'player_1_pretrained_adversary_sb': './results/checkpoint-adv-external-3273-player_1.pkl'}, 'batch_mode': 'complete_episodes', 'train_batch_size': 256, 'lr': 0, 'sgd_minibatch_size': 256, 'num_sgd_iter': 1, 'rollout_fragment_length': 200, 'run_uid': '_setme', 'http_remote_port': 'http://127.0.0.1:50001', 'num_workers': 0, '_p_normal': 0.5, '_verbose': False, 'multiagent': {'policies': {'player_1_pretrained_adversary_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': 'normalization_only'}}, 'framework': 'tfe'}), 'player_1_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 0, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_0', 'load_weights': True}}, 'framework': 'tfe'}), 'player_2_pretrained_sb': (<class 'ray.rllib.policy.tf_policy_template.PPOTFPolicy'>, Box(380,), Box(17,), {'model': {'custom_model': 'GymCompetePretrainedModel', 'custom_model_config': {'agent_id': 1, 'env_name': 'multicomp/YouShallNotPassHumans-v0', 'model_config': {}, 'name': 'model_1', 'load_weights': True}}, 'framework': 'tfe'})}}})}, 'tf_session_args': {'intra_op_parallelism_threads': 4, 'inter_op_parallelism_threads': 4, 'gpu_options': {'allow_growth': True}, 'log_device_placement': True, 'device_count': {'CPU': 4}, 'allow_soft_placement': True}, 'local_tf_session_args': {'intra_op_parallelism_threads': 4, 'inter_op_parallelism_threads': 4}, 'kl_coeff': 1.0, 'use_gae': True, 'num_gpus': 0, 'framework': 'tfe', 'num_envs_per_worker': 1, 'batch_mode': 'complete_episodes', 'train_batch_size': 256, 'lr': 0, 'sgd_minibatch_size': 256, 'num_sgd_iter': 1, 'rollout_fragment_length': 200, 'run_uid': '_setme', 'http_remote_port': 'http://127.0.0.1:50001', 'num_workers': 0}\n",
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /scratch/sergei/better-adversarial-defenses/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n",
      "Not setting NN weights\n",
      "Loading normalization\n",
      "WARNING:tensorflow:From /scratch/sergei/miniconda3/lib/python3.7/site-packages/ray/rllib/models/tf/tf_action_dist.py:234: to_float (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "Setting NN weights\n",
      "Loading normalization\n",
      "Setting NN weights\n",
      "Loading normalization\n",
      "  0%|                                                    | 0/10 [00:00<?, ?it/s]WARNING:tensorflow:From /scratch/sergei/miniconda3/lib/python3.7/site-packages/ray/rllib/policy/tf_policy.py:761: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Prefer Variable.assign which has equivalent behavior in 2.X.\n",
      "100%|███████████████████████████████████████████| 10/10 [07:17<00:00, 43.71s/it]\n",
      "{'episode_reward': [6.221853831575572, 0.20327966687909793, 6.076482087330078, 6.072269364231033, 3.991483121597412, 6.106027821166208, 6.372146715730196, 5.9787216528638965, -1.8159099524818885, 0.5262512255721958, 6.352657357278076, -1.319223416110617, 6.109550450921233, -1.227716326706286, 5.5257980780734215, 2.801333043728846, 6.402926458220463, 6.323545454186387, 6.357950117344444, -0.8406751611619256, 6.0495523309509736], 'policy_player_1_pretrained_adversary_sb_reward': [-10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0, -10.0], 'policy_player_2_pretrained_sb_reward': [16.221853831575572, -9.796720333120902, 16.076482087330078, 16.072269364231033, 13.991483121597412, 16.106027821166208, 16.372146715730196, 15.978721652863896, -11.815909952481888, 10.526251225572196, 16.352657357278076, -11.319223416110617, 16.109550450921233, -11.227716326706286, 15.525798078073421, 12.801333043728846, 16.402926458220463, 16.323545454186387, 16.357950117344444, -10.840675161161926, 16.049552330950974], 'policy_player_1_pretrained_sb_reward': [10.0, -10.0, 10.0, -10.0, 10.0, 10.0, -10.0, -10.0, 10.0, -10.0]}\n",
      "Player episode_reward total trials 21 win rate 80.95238095238095% loss rate 19.047619047619047% tie rate 0.0%\n",
      "Player policy_player_1_pretrained_adversary_sb_reward total trials 11 win rate 0.0% loss rate 100.0% tie rate 0.0%\n",
      "Player policy_player_2_pretrained_sb_reward total trials 21 win rate 76.19047619047619% loss rate 23.80952380952381% tie rate 0.0%\n",
      "Player policy_player_1_pretrained_sb_reward total trials 10 win rate 50.0% loss rate 50.0% tie rate 0.0%\n",
      "Your video is in\n",
      "/scratch/sergei/better-adversarial-defenses/experiment_analysis/video-20200922-110540-39a56ca4-fcfe-11ea-8c46-e0d55e6af99c/video.000000.mp4\n"
     ]
    }
   ],
   "source": [
    "!export DISPLAY=:0; python ../make_video.py --checkpoint {checkpoint} --config=victim_recover_withnormal_sb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "#video = _\n",
    "video = \"video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Click here to download: <a href='video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4' target='_blank'>video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4</a><br>"
      ],
      "text/plain": [
       "/scratch/sergei/better-adversarial-defenses/experiment_analysis/video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, FileLink\n",
    "\n",
    "local_file = FileLink(video, result_html_prefix=\"Click here to download: \")\n",
    "display(local_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <video alt=\"test\" controls>\n",
       "        <source src=\"video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4\" type=\"video/mp4\">\n",
       "    </video>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML(f\"\"\"\n",
    "    <video alt=\"test\" controls>\n",
    "        <source src=\"{video}\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n    <video alt=\"test\" controls>\\n        <source src=\"experiment_analysis/video-20200918-135137-beae468e-f9f0-11ea-b316-e0d55e6af99c/video.000000.mp4\" type=\"video/mp4\">\\n    </video>\\n'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f\"\"\"\n",
    "    <video alt=\"test\" controls>\n",
    "        <source src=\"{video}\" type=\"video/mp4\">\n",
    "    </video>\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}