demo09-rl-basics.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHvX1yMS_S0F"
      },
      "source": [
        "# Reinforcement Learning Basics\n",
        "\n",
        "In this demo, we will play around with the environments available through [Gym](https://www.gymlibrary.dev/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NMlOGDB66SLd"
      },
      "source": [
        "## Libraries\n",
        "\n",
        "For visualizing our agent playing, we'll need to load the following library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MBv6R70V6FaU",
        "outputId": "9b07df85-749b-42e4-8122-f1df665f6736"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imageio==2.4.1 in /usr/local/lib/python3.7/dist-packages (2.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (1.21.6)\n",
            "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from imageio==2.4.1) (7.1.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: gym[classic_control] in /usr/local/lib/python3.7/dist-packages (0.25.2)\n",
            "Requirement already satisfied: gym-notices>=0.0.4 in /usr/local/lib/python3.7/dist-packages (from gym[classic_control]) (0.0.8)\n",
            "Requirement already satisfied: numpy>=1.18.0 in /usr/local/lib/python3.7/dist-packages (from gym[classic_control]) (1.21.6)\n",
            "Requirement already satisfied: importlib-metadata>=4.8.0 in /usr/local/lib/python3.7/dist-packages (from gym[classic_control]) (4.13.0)\n",
            "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym[classic_control]) (1.5.0)\n",
            "Collecting pygame==2.1.0\n",
            "  Downloading pygame-2.1.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 18.3 MB 96 kB/s \n",
            "\u001b[?25hRequirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.8.0->gym[classic_control]) (4.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.8.0->gym[classic_control]) (3.10.0)\n",
            "Installing collected packages: pygame\n",
            "Successfully installed pygame-2.1.0\n"
          ]
        }
      ],
      "source": [
        "!pip3 install imageio==2.4.1\n",
        "!pip3 install gym[classic_control]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3IjJbnbd_Ski",
        "outputId": "f6255be6-a4c1-42cb-d6f7-dc76c334be76"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Imageio: 'ffmpeg-linux64-v3.3.1' was not found on your computer; downloading it now.\n",
            "Try 1. Download from https://github.com/imageio/imageio-binaries/raw/master/ffmpeg/ffmpeg-linux64-v3.3.1 (43.8 MB)\n",
            "Downloading: 8192/45929032 bytes (0.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b1327104/45929032 bytes (2.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b3727360/45929032 bytes (8.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b5840896/45929032 bytes (12.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b7225344/45929032 bytes (15.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b9052160/45929032 bytes (19.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b10887168/45929032 bytes (23.7%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b13041664/45929032 bytes (28.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b15097856/45929032 bytes (32.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b17514496/45929032 bytes (38.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b19800064/45929032 bytes (43.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b23150592/45929032 bytes (50.4%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b26329088/45929032 bytes (57.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b29859840/45929032 bytes (65.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b32899072/45929032 bytes (71.6%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b34684928/45929032 bytes (75.5%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b36872192/45929032 bytes (80.3%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b38600704/45929032 bytes (84.0%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b41852928/45929032 bytes (91.1%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45424640/45929032 bytes (98.9%)\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b45929032/45929032 bytes (100.0%)\n",
            "  Done\n",
            "File saved as /root/.imageio/ffmpeg/ffmpeg-linux64-v3.3.1.\n"
          ]
        }
      ],
      "source": [
        "# Libraries\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.optim as optim\n",
        "import torch.nn.functional as F\n",
        "import torch.distributions as distributions\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import gym\n",
        "\n",
        "import moviepy.video.io.ImageSequenceClip\n",
        "from moviepy.editor import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EybNyRkA_1RR"
      },
      "source": [
        "Check out a list of classic games [here](https://www.gymlibrary.dev/environments/classic_control/). We'll be experimenting with Cart Pole where the object is to keep a pole on top of a cart without falling."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaQ8vbW3_cJd",
        "outputId": "a3e518e5-527f-4d16-f4a8-97be54555d60"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.7/dist-packages/gym/core.py:318: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
            "  \"Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\"\n",
            "\n",
            "WARNING:py.warnings:/usr/local/lib/python3.7/dist-packages/gym/wrappers/step_api_compatibility.py:40: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
            "  \"Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\"\n",
            "\n"
          ]
        }
      ],
      "source": [
        "env = gym.make('CartPole-v1')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NteLTpFkAIoD"
      },
      "source": [
        "The observation space consits of four variables:\n",
        "\n",
        "* Cart position (on a line)\n",
        "\n",
        "* Cart velocity\n",
        "\n",
        "* Pole angle\n",
        "\n",
        "* Pole angular velocity\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A68_Rdpg_ksA",
        "outputId": "f8bf943f-426c-4617-888c-b3a8fda3a666"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Box([-4.8000002e+00 -3.4028235e+38 -4.1887903e-01 -3.4028235e+38], [4.8000002e+00 3.4028235e+38 4.1887903e-01 3.4028235e+38], (4,), float32)\n",
            "(4,)\n"
          ]
        }
      ],
      "source": [
        "print(env.observation_space)\n",
        "print(env.observation_space.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaAOhodQAXZn"
      },
      "source": [
        "We can take two actions: either push the cart to the left or to the right."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EXHGYyZI_pbF",
        "outputId": "530717aa-3229-411c-e219-d51e1e1098cf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Discrete(2)\n"
          ]
        }
      ],
      "source": [
        "print(env.action_space)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lN6M5MHfBotA"
      },
      "source": [
        "The game is actually pretty hard! You can try your hand at it [here](https://jeffjar.me/cartpole.html). Now let's train a policy to beat it. We start by loading two different copies of the environment; one for training and one for testing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "aLnTBj36BKx_"
      },
      "outputs": [],
      "source": [
        "train_env = gym.make('CartPole-v1')\n",
        "test_env = gym.make('CartPole-v1')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZY4apShkM7gO"
      },
      "source": [
        "We'll train a neural network that is actually incredibly simple: only two layers with dropout and ReLU."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "Qf6OfYtfBQhf"
      },
      "outputs": [],
      "source": [
        "class PolicyNet(nn.Module):\n",
        "  def __init__(self, input_dim, hidden_dim, output_dim, dropout=.5):\n",
        "    super().__init__()\n",
        "    self.net = nn.Sequential(\n",
        "        nn.Linear(input_dim, hidden_dim),\n",
        "        nn.Dropout(dropout),\n",
        "        nn.ReLU(),\n",
        "        nn.Linear(hidden_dim, output_dim)\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    return self.net(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-MffYm5bNB8P"
      },
      "source": [
        "As we've done many times before, we initialize our architecture and optimizer. However, the loss will be slightly more tricky to compute than normal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aH7BxehjCME_",
        "outputId": "905d6c65-34f7-4c0d-9c28-be606db61015"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "input dim: 4\n",
            "output dim: 2\n",
            "PolicyNet(\n",
            "  (net): Sequential(\n",
            "    (0): Linear(in_features=4, out_features=128, bias=True)\n",
            "    (1): Dropout(p=0.5, inplace=False)\n",
            "    (2): ReLU()\n",
            "    (3): Linear(in_features=128, out_features=2, bias=True)\n",
            "  )\n",
            ")\n"
          ]
        }
      ],
      "source": [
        "input_dim = train_env.observation_space.shape[0]\n",
        "print('input dim:', input_dim)\n",
        "\n",
        "output_dim = train_env.action_space.n\n",
        "print('output dim:', output_dim)\n",
        "\n",
        "hidden_dim = 128\n",
        "\n",
        "policy = PolicyNet(input_dim, hidden_dim, output_dim)\n",
        "print(policy)\n",
        "\n",
        "optimizer = optim.Adam(policy.parameters(), lr=0.01)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OqDXMa7FNKzp"
      },
      "source": [
        "We will write a function for calculating a sequence of returns. With these in hand, we can calculate the empirical average over many trajectories as described in the lecture [notes](https://chinmayhegde.github.io/dl-notes/notes/lecture09/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "7aoz9zsmC191"
      },
      "outputs": [],
      "source": [
        "def calculate_returns(rewards, discount_factor, normalize=True):\n",
        "  returns = []\n",
        "  R = 0\n",
        "  for r in reversed(rewards):\n",
        "    R = r + R * discount_factor\n",
        "    returns.insert(0,R)\n",
        "\n",
        "  returns = torch.tensor(returns)\n",
        "  # Remember we normalize to differentiate between\n",
        "  # \"good\" and \"bad\" actions\n",
        "  if normalize:\n",
        "    returns = (returns - returns.mean()) / returns.std()\n",
        "  \n",
        "  return returns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "49fQ08FqNyK8"
      },
      "source": [
        "Once we have returns and log probabilities of the actions we take, we will compute the loss and optimize."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "-lb4sQzsDmQa"
      },
      "outputs": [],
      "source": [
        "def update_policy(returns, log_prob_actions, optimizer):\n",
        "  returns = returns.detach()\n",
        "  # Because we minimize (by convention) and we actually\n",
        "  # want to maximize returns, we negate the expected sum\n",
        "  loss = - (returns * log_prob_actions).sum()\n",
        "  optimizer.zero_grad()\n",
        "\n",
        "  loss.backward()\n",
        "  optimizer.step()\n",
        "\n",
        "  return loss.item()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ei4mKrz1OPcE"
      },
      "source": [
        "Now we write the training and evaluation functions. Notice how training in RL is more involved than our standard applications."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "5RynMcojD37z"
      },
      "outputs": [],
      "source": [
        "def train(env, policy, optimizer, discount_factor=.99):\n",
        "  policy.train()\n",
        "  \n",
        "  log_prob_actions = []\n",
        "  rewards = []\n",
        "  done = False\n",
        "  episode_reward = 0\n",
        "\n",
        "  state = env.reset()\n",
        "\n",
        "  while not done:\n",
        "    state = torch.FloatTensor(state).unsqueeze(0)\n",
        "    action_pred = policy(state)\n",
        "    action_prob = F.softmax(action_pred, dim = -1)\n",
        "\n",
        "    dist = distributions.Categorical(action_prob)\n",
        "    action = dist.sample() # we sample (rather than take the best)\n",
        "    log_prob_action = dist.log_prob(action)\n",
        "\n",
        "    state, reward, done, _ = env.step(action.item())\n",
        "\n",
        "    log_prob_actions.append(log_prob_action)\n",
        "    rewards.append(reward)\n",
        "\n",
        "    episode_reward += reward\n",
        "  \n",
        "  log_prob_actions = torch.cat(log_prob_actions)\n",
        "  returns = calculate_returns(rewards, discount_factor)\n",
        "  loss = update_policy(returns, log_prob_actions, optimizer)\n",
        "\n",
        "  return loss, episode_reward\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "xEB0VAtCDrwE"
      },
      "outputs": [],
      "source": [
        "def evaluate(env, policy, return_frames=False):\n",
        "  policy.eval()\n",
        "\n",
        "  done = False\n",
        "  episode_reward = 0\n",
        "  state = env.reset()\n",
        "  frames = []\n",
        "\n",
        "  while not done:\n",
        "    if return_frames:\n",
        "      frames += [env.render(mode='rgb_array')]\n",
        "    state = torch.FloatTensor(state).unsqueeze(0)\n",
        "\n",
        "    action_pred = policy(state)\n",
        "    action_prob = F.softmax(action_pred, dim=-1)\n",
        "\n",
        "    action = torch.argmax(action_prob, dim=-1) # we take the best\n",
        "    state, reward, done, _ = env.step(action.item())\n",
        "    episode_reward += reward\n",
        "  \n",
        "  if return_frames:\n",
        "    return episode_reward, frames\n",
        "  return episode_reward"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlkAPl65OgMz"
      },
      "source": [
        "Now we're ready to train. Notice the very jagged nature of the loss history. This is because RL is different from our regular training: our policy is stochastic and the reward is more \"sparse\"."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9G4aAv6tK1to",
        "outputId": "b8a32160-c404-4a97-b05a-cfca08842138"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "| Episode:  10 | Mean Train Rewards:  21.4 | Mean Test Rewards:   9.4 |\n",
            "| Episode:  20 | Mean Train Rewards:  15.9 | Mean Test Rewards:   9.3 |\n",
            "| Episode:  30 | Mean Train Rewards:  11.2 | Mean Test Rewards:   9.2 |\n",
            "| Episode:  40 | Mean Train Rewards:  12.0 | Mean Test Rewards:   9.3 |\n",
            "| Episode:  50 | Mean Train Rewards:  20.0 | Mean Test Rewards:  23.7 |\n",
            "| Episode:  60 | Mean Train Rewards:  36.9 | Mean Test Rewards:  48.7 |\n",
            "| Episode:  70 | Mean Train Rewards:  53.6 | Mean Test Rewards:  93.6 |\n",
            "| Episode:  80 | Mean Train Rewards:  55.8 | Mean Test Rewards: 133.5 |\n",
            "| Episode:  90 | Mean Train Rewards:  78.9 | Mean Test Rewards: 144.8 |\n",
            "| Episode: 100 | Mean Train Rewards:  95.2 | Mean Test Rewards: 181.8 |\n",
            "| Episode: 110 | Mean Train Rewards: 104.7 | Mean Test Rewards: 191.0 |\n",
            "| Episode: 120 | Mean Train Rewards: 149.4 | Mean Test Rewards: 298.0 |\n",
            "| Episode: 130 | Mean Train Rewards: 233.9 | Mean Test Rewards: 404.8 |\n",
            "Reached reward threshold in 136 episodes\n"
          ]
        }
      ],
      "source": [
        "max_episodes = 500\n",
        "num_trials = 25\n",
        "reward_threshold = 475\n",
        "print_every = 10\n",
        "\n",
        "train_rewards = []\n",
        "test_rewards = []\n",
        "\n",
        "for episode in range(1, max_episodes+1):\n",
        "  loss, train_reward = train(train_env, policy, optimizer)\n",
        "  test_reward = evaluate(test_env, policy)\n",
        "\n",
        "  train_rewards += [train_reward]\n",
        "  test_rewards += [test_reward]\n",
        "\n",
        "  mean_train_rewards = np.mean(train_rewards[-num_trials:])\n",
        "  mean_test_rewards = np.mean(test_rewards[-num_trials:])\n",
        "\n",
        "  if episode % print_every == 0:    \n",
        "    print(f'| Episode: {episode:3} | Mean Train Rewards: {mean_train_rewards:5.1f} | Mean Test Rewards: {mean_test_rewards:5.1f} |')\n",
        "    \n",
        "  if mean_test_rewards >= reward_threshold:\n",
        "    print(f'Reached reward threshold in {episode} episodes')\n",
        "    break\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 506
        },
        "id": "-txcei7ULrTE",
        "outputId": "68425584-bbd8-48e0-e11b-998079ffa30c"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.plot(test_rewards, label='Test Reward')\n",
        "plt.plot(train_rewards, label='Train Reward')\n",
        "plt.xlabel('Episode', fontsize=20)\n",
        "plt.ylabel('Reward', fontsize=20)\n",
        "plt.hlines(reward_threshold, 0, len(test_rewards), color='r')\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WA__SEEl6or-",
        "outputId": "6e9a0bf0-f928-48ac-8dcf-ae323cc96add"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:py.warnings:/usr/local/lib/python3.7/dist-packages/gym/core.py:44: DeprecationWarning: \u001b[33mWARN: The argument mode in render method is deprecated; use render_mode during environment initialization instead.\n",
            "See here for more information: https://www.gymlibrary.ml/content/api/\u001b[0m\n",
            "  \"The argument mode in render method is deprecated; \"\n",
            "\n"
          ]
        }
      ],
      "source": [
        "_, frames = evaluate(test_env, policy, return_frames = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 439
        },
        "id": "dUGfR7JX68-0",
        "outputId": "72876e63-6196-4252-ee05-750d9dae0c8c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 422/422 [00:02<00:00, 140.77it/s]\n"
          ]
        },
        {
          "data": {
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ],
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(frames, fps=50)\n",
        "ipython_display(clip)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.10.0 64-bit ('3.10.0')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.10.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "6aea8d84a86a1280a34780710c5e478b9c6b3c016841b907182e7484363820b1"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}