04.TD3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmj6YpybPggi"
      },
      "source": [
        "## Configuration for Colab"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjqeHnaKPggj",
        "outputId": "84d8f310-5220-491e-fba2-1ac5ff202b5c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting gymnasium==1.0.0\n",
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            "Collecting farama-notifications>=0.0.1 (from gymnasium==1.0.0)\n",
            "  Downloading Farama_Notifications-0.0.4-py3-none-any.whl.metadata (558 bytes)\n",
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            "\u001b[?25hDownloading Farama_Notifications-0.0.4-py3-none-any.whl (2.5 kB)\n",
            "Installing collected packages: farama-notifications, gymnasium\n",
            "Successfully installed farama-notifications-0.0.4 gymnasium-1.0.0\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "\n",
        "IN_COLAB = \"google.colab\" in sys.modules\n",
        "\n",
        "if IN_COLAB:\n",
        "    !pip install gymnasium==1.0.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DIGopM6lPggk"
      },
      "source": [
        "# 04. TD3\n",
        "\n",
        "[Fujimoto, Scott, Herke van Hoof, and David Meger. \"Addressing function approximation error in actor-critic methods.\" arXiv preprint arXiv:1802.09477 2018.](https://arxiv.org/pdf/1802.09477.pdf)\n",
        "\n",
        "In value-based reinforcement learning methods, function approximation errors are known to lead to overestimated value estimates and suboptimal policies. However, similar issues with actor-critic methods in continuous control domains have been largely left untouched (See paper for detailed description). To solve this problem, this paper proposes *a clipped Double Q-learning*. In addtion, this paper contains a number of components that address variance reduction.\n",
        "\n",
        "The author's modifications are applied to actor-critic method for continuous control, Deep Deterministic Policy Gradient algorithm ([DDPG](https://arxiv.org/pdf/1509.02971.pdf)), to form the *Twin Delayed Deep Deterministic policy gradient algorithm (TD3)*.\n",
        "\n",
        "### DDPG\n",
        "For learning in high-dimentional and continous action spaces, the authors of DDPG combine the actor-critic approach with insights from the success of DQN. Deep DPG(DDPG) is based on the deterministic policy gradient(DPG) algorithm ([Silver et al., 2014](http://proceedings.mlr.press/v32/silver14.pdf)). Please see *03.DDPG.ipynb* for detailed description of DDPG.\n",
        "\n",
        "### Double Q-learning\n",
        "In Double DQN ([Van Hasselt et al., 2016](https://arxiv.org/pdf/1509.06461.pdf)), the authors propose using the target network as one of the value estimates, and obtain a policy by greedy maximization of the current value network rather than the target network. In an actor-critic setting, an analogous update uses the current policy rather than the target policy in the learning target. However, with the slow-changing policy in actor-critic, the current and target networks were too similar to make an independent estimation, and offered little improvement. Instead, the original Double Q-learning formulation can be used, with a pair of actors $(\\pi_{\\phi_1}, \\pi_{\\phi_2})$ and critics $(Q_{\\theta_1}, Q_{\\theta_2})$, where $\\pi_{\\phi_1}$ is optimized with respect to $Q_{\\theta_1}$ and $\\pi_{\\phi_2}$ with respect to $Q_{\\theta_2}$:\n",
        "\n",
        "$$\n",
        "y_1 = r + \\gamma Q_{\\theta'_2} (s' , \\pi_{\\phi_1}(s')) \\\\\n",
        "y_2 = r + \\gamma Q_{\\theta'_1} (s' , \\pi_{\\phi_2}(s'))\n",
        "$$\n",
        "\n",
        "### A clipped Double Q-learning\n",
        "The critics are not entirely independent, due to the use of the opposite critic in the learning targets, as well as the same replay buffer. As a result, for some states we will have $Q_{\\theta'_2}(s, \\pi_{\\phi_1}) > Q_{\\theta'_1}(s, \\pi_{\\phi_1})$. This is problematic because $Q_{\\theta'_1}(s, \\pi_{\\phi_1})$ will generally overestimate the true value, and in certain areas of the state space the overestimation will be further exaggerated. To address this problem, the authors propose to take the minimum between the two estimates:\n",
        "\n",
        "$$\n",
        "y_1 = r + \\gamma \\underset{i=1,2}{\\min} Q_{\\theta'_i} (s' , \\pi_{\\phi_1}(s'))\n",
        "$$\n",
        "\n",
        "### Delayed Policy Updates\n",
        "If policy updates on high-error states cause different behavior, then the policy network should be updated at a lower frequency than the value network, to first minimize error before introducing a policy update. The authors propose delaying policy updates until the value error is as small as possible.\n",
        "\n",
        "### Target Policy Smoothing Regularization\n",
        "When updating the critic, a learning target using a deterministic policy is highly susceptible to in accuracies induced by function approximation error, increasing the variance of the target. This induced variance can be reduced through regularization. The authors propose that fitting the value of a small area around the target action\n",
        "\n",
        "$$\n",
        "y = r + E_\\epsilon [Q_{\\theta'}(s', \\pi_{\\phi '}(s') + \\epsilon],\n",
        "$$\n",
        "\n",
        "would have the benefit of smoothing the value estimate by bootstrapping off of similar state-action value estimates. In practice, this makes below:\n",
        "\n",
        "$$\n",
        "y = r + \\gamma Q_{\\theta '}(s', \\pi_{\\phi '}(s') + \\epsilon), \\\\\n",
        "\\epsilon \\sim \\text{clip} (\\mathcal(N)(0, \\sigma), -c, c),\n",
        "$$\n",
        "\n",
        "where the added noise is clipped to keep the target close tothe original action."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dO5NkLRePggl"
      },
      "source": [
        "## import module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "f4DQHLwlPggl"
      },
      "outputs": [],
      "source": [
        "import copy\n",
        "import os\n",
        "import random\n",
        "from typing import Dict, List, Tuple\n",
        "\n",
        "import gymnasium as gym\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from IPython.display import clear_output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XAXt8mj-Pggl"
      },
      "source": [
        "## Replay buffer\n",
        "Typically, people implement replay buffers with one of the following three data structures:\n",
        "\n",
        "- collections.deque\n",
        "- list\n",
        "- numpy.ndarray\n",
        "\n",
        "**deque** is very easy to handle once you initialize its maximum length (e.g. deque(maxlen=buffer_size)). However, the indexing operation of deque gets terribly slow as it grows up because it is [internally doubly linked list](https://wiki.python.org/moin/TimeComplexity#collections.deque). On the other hands, **list** is an array, so it is relatively faster than deque when you sample batches at every step. Its amortized cost of Get item is [O(1)](https://wiki.python.org/moin/TimeComplexity#list).\n",
        "\n",
        "Last but not least, let's see **numpy.ndarray**. numpy.ndarray is even faster than list due to the fact that it is [a homogeneous array of fixed-size items](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray), so you can get the benefits of [locality of reference](https://en.wikipedia.org/wiki/Locality_of_reference), . Whereas list is an array of pointers to objects, even when all of them are of the same type.\n",
        "\n",
        "Here, we are going to implement a replay buffer using numpy.ndarray.\n",
        "\n",
        "Reference:\n",
        "- [OpenAI spinning-up](https://github.com/openai/spinningup/blob/master/spinup/algos/sac/sac.py#L10)\n",
        "- [rainbow-is-all-you-need](https://render.githubusercontent.com/view/ipynb?commit=032d11277cf2436853478a69ca5a4aba03202598&enc_url=68747470733a2f2f7261772e67697468756275736572636f6e74656e742e636f6d2f437572742d5061726b2f7261696e626f772d69732d616c6c2d796f752d6e6565642f303332643131323737636632343336383533343738613639636135613461626130333230323539382f30312e64716e2e6970796e62&nwo=Curt-Park%2Frainbow-is-all-you-need&path=01.dqn.ipynb&repository_id=191133946&repository_type=Repository#Replay-buffer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Ly-vgoJ0Pggl"
      },
      "outputs": [],
      "source": [
        "class ReplayBuffer:\n",
        "    \"\"\"A simple numpy replay buffer.\"\"\"\n",
        "\n",
        "    def __init__(self, obs_dim: int, act_dim: int, size: int, batch_size: int = 32):\n",
        "        \"\"\"Initializate.\"\"\"\n",
        "        self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
        "        self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
        "        self.acts_buf = np.zeros([size, act_dim], dtype=np.float32)\n",
        "        self.rews_buf = np.zeros([size], dtype=np.float32)\n",
        "        self.done_buf = np.zeros([size], dtype=np.float32)\n",
        "        self.max_size, self.batch_size = size, batch_size\n",
        "        self.ptr, self.size = 0, 0\n",
        "\n",
        "    def store(\n",
        "        self,\n",
        "        obs: np.ndarray,\n",
        "        act: np.ndarray,\n",
        "        rew: float,\n",
        "        next_obs: np.ndarray,\n",
        "        done: bool,\n",
        "    ):\n",
        "        \"\"\"Store the transition in buffer.\"\"\"\n",
        "        self.obs_buf[self.ptr] = obs\n",
        "        self.next_obs_buf[self.ptr] = next_obs\n",
        "        self.acts_buf[self.ptr] = act\n",
        "        self.rews_buf[self.ptr] = rew\n",
        "        self.done_buf[self.ptr] = done\n",
        "        self.ptr = (self.ptr + 1) % self.max_size\n",
        "        self.size = min(self.size + 1, self.max_size)\n",
        "\n",
        "    def sample_batch(self) -> Dict[str, np.ndarray]:\n",
        "        \"\"\"Randomly sample a batch of experiences from memory.\"\"\"\n",
        "        idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
        "        return dict(\n",
        "            obs=self.obs_buf[idxs],\n",
        "            next_obs=self.next_obs_buf[idxs],\n",
        "            acts=self.acts_buf[idxs],\n",
        "            rews=self.rews_buf[idxs],\n",
        "            done=self.done_buf[idxs],\n",
        "        )\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return self.size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlEggFvFPggl"
      },
      "source": [
        "## Gaussian Noise\n",
        "Because the DDPG and the TD3 policy is deterministic, it's not enough to explore a wide variety of actions. In order to facilitate more exploration. TD3 adds Gaussian noise to each action, while DDPG uses Ornstein-Uhlenbeck noise. The TD3 paper states Ornstein-Uhlenbeck noise offered no performance benefits."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "bUT5eWg6Pggl"
      },
      "outputs": [],
      "source": [
        "class GaussianNoise:\n",
        "    \"\"\"Gaussian Noise.\n",
        "    Taken from https://github.com/vitchyr/rlkit\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        action_dim: int,\n",
        "        min_sigma: float = 1.0,\n",
        "        max_sigma: float = 1.0,\n",
        "        decay_period: int = 1000000,\n",
        "    ):\n",
        "        \"\"\"Initialize.\"\"\"\n",
        "        self.action_dim = action_dim\n",
        "        self.max_sigma = max_sigma\n",
        "        self.min_sigma = min_sigma\n",
        "        self.decay_period = decay_period\n",
        "\n",
        "    def sample(self, t: int = 0) -> float:\n",
        "        \"\"\"Get an action with gaussian noise.\"\"\"\n",
        "        sigma = self.max_sigma - (self.max_sigma - self.min_sigma) * min(1.0, t / self.decay_period)\n",
        "        return np.random.normal(0, sigma, size=self.action_dim)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nEXGUpYPPggm"
      },
      "source": [
        "## Network\n",
        "We are going to use two separated networks for actor and critic. The actor network has three fully connected layers and three non-linearity functions, *ReLU* for hidden layers and *tanh* for the output layer. On the other hand, the critic network has three fully connected layers, but it used two activation functions for hidden layers *ReLU*. Plus, its input sizes of critic network are sum of state sizes and action sizes. One thing to note is that we initialize the final layer's weights and biases so that they are *uniformly distributed.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "U0oDM0VLPggm"
      },
      "outputs": [],
      "source": [
        "class Actor(nn.Module):\n",
        "    def __init__(self, in_dim: int, out_dim: int, init_w: float = 3e-3):\n",
        "        \"\"\"Initialize.\"\"\"\n",
        "        super(Actor, self).__init__()\n",
        "\n",
        "        self.hidden1 = nn.Linear(in_dim, 128)\n",
        "        self.hidden2 = nn.Linear(128, 128)\n",
        "        self.out = nn.Linear(128, out_dim)\n",
        "\n",
        "        self.out.weight.data.uniform_(-init_w, init_w)\n",
        "        self.out.bias.data.uniform_(-init_w, init_w)\n",
        "\n",
        "    def forward(self, state: torch.Tensor) -> torch.Tensor:\n",
        "        \"\"\"Forward method implementation.\"\"\"\n",
        "        x = F.relu(self.hidden1(state))\n",
        "        x = F.relu(self.hidden2(x))\n",
        "        action = self.out(x).tanh()\n",
        "\n",
        "        return action\n",
        "\n",
        "\n",
        "class Critic(nn.Module):\n",
        "    def __init__(self, in_dim: int, init_w: float = 3e-3):\n",
        "        \"\"\"Initialize.\"\"\"\n",
        "        super(Critic, self).__init__()\n",
        "\n",
        "        self.hidden1 = nn.Linear(in_dim, 128)\n",
        "        self.hidden2 = nn.Linear(128, 128)\n",
        "        self.out = nn.Linear(128, 1)\n",
        "\n",
        "        self.out.weight.data.uniform_(-init_w, init_w)\n",
        "        self.out.bias.data.uniform_(-init_w, init_w)\n",
        "\n",
        "    def forward(self, state: torch.Tensor, action: torch.Tensor) -> torch.Tensor:\n",
        "        \"\"\"Forward method implementation.\"\"\"\n",
        "        x = torch.cat((state, action), dim=-1)\n",
        "        x = F.relu(self.hidden1(x))\n",
        "        x = F.relu(self.hidden2(x))\n",
        "        value = self.out(x)\n",
        "\n",
        "        return value"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8jQ75q_Pggm"
      },
      "source": [
        "## TD3Agent\n",
        "Here is a summary of TD3Agent class.\n",
        "\n",
        "| Method           | Note                                                 |\n",
        "|---               |---                                                  |\n",
        "|select_action     | select an action from the input state.               |\n",
        "|step              | take an action and return the response of the env.   |\n",
        "|update_model      | update the model by gradient descent.                |\n",
        "|train             | train the agent during num_frames.                   |\n",
        "|test              | test the agent (1 episode).                          |\n",
        "|\\_target_soft_update| soft update from the local model to the target model.|\n",
        "|\\_plot              | plot the training progresses.                        |"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "q-87beELPggm"
      },
      "outputs": [],
      "source": [
        "class TD3Agent:\n",
        "    \"\"\"TD3Agent interacting with environment.\n",
        "\n",
        "    Attribute:\n",
        "        env (gym.Env): openAI Gym environment\n",
        "        actor1 (nn.Module): target actor model to select actions\n",
        "        actor2 (nn.Module): target actor model to select actions\n",
        "        actor_target1 (nn.Module): actor model to predict next actions\n",
        "        actor_target2 (nn.Module): actor model to predict next actions\n",
        "        actor_optimizer (Optimizer): optimizer for training actor\n",
        "        critic1 (nn.Module): critic model to predict state values\n",
        "        critic2 (nn.Module): critic model to predict state values\n",
        "        critic_target1 (nn.Module): target critic model to predict state values\n",
        "        critic_target2 (nn.Module): target critic model to predict state values\n",
        "        critic_optimizer (Optimizer): optimizer for training critic\n",
        "        memory (ReplayBuffer): replay memory to store transitions\n",
        "        batch_size (int): batch size for sampling\n",
        "        gamma (float): discount factor\n",
        "        tau (float): parameter for soft target update\n",
        "        initial_random_steps (int): initial random action steps\n",
        "        exploration_noise (GaussianNoise): gaussian noise for policy\n",
        "        target_policy_noise (GaussianNoise): gaussian noise for target policy\n",
        "        target_policy_noise_clip (float): clip target gaussian noise\n",
        "        device (torch.device): cpu / gpu\n",
        "        transition (list): temporory storage for the recent transition\n",
        "        policy_update_freq (int): update actor every time critic updates this times\n",
        "        total_step (int): total step numbers\n",
        "        is_test (bool): flag to show the current mode (train / test)\n",
        "        seed (int): random seed\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        env: gym.Env,\n",
        "        memory_size: int,\n",
        "        batch_size: int,\n",
        "        gamma: float = 0.99,\n",
        "        tau: float = 5e-3,\n",
        "        exploration_noise: float = 0.1,\n",
        "        target_policy_noise: float = 0.2,\n",
        "        target_policy_noise_clip: float = 0.5,\n",
        "        initial_random_steps: int = int(1e4),\n",
        "        policy_update_freq: int = 2,\n",
        "        seed: int = 777,\n",
        "    ):\n",
        "        \"\"\"Initialize.\"\"\"\n",
        "        obs_dim = env.observation_space.shape[0]\n",
        "        action_dim = env.action_space.shape[0]\n",
        "\n",
        "        self.env = env\n",
        "        self.memory = ReplayBuffer(obs_dim, action_dim, memory_size, batch_size)\n",
        "        self.batch_size = batch_size\n",
        "        self.gamma = gamma\n",
        "        self.tau = tau\n",
        "        self.initial_random_steps = initial_random_steps\n",
        "        self.policy_update_freq = policy_update_freq\n",
        "        self.seed = seed\n",
        "\n",
        "        # device: cpu / gpu\n",
        "        self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "        print(self.device)\n",
        "\n",
        "        # noise\n",
        "        self.exploration_noise = GaussianNoise(action_dim, exploration_noise, exploration_noise)\n",
        "        self.target_policy_noise = GaussianNoise(\n",
        "            action_dim, target_policy_noise, target_policy_noise\n",
        "        )\n",
        "        self.target_policy_noise_clip = target_policy_noise_clip\n",
        "\n",
        "        # networks\n",
        "        self.actor = Actor(obs_dim, action_dim).to(self.device)\n",
        "        self.actor_target = Actor(obs_dim, action_dim).to(self.device)\n",
        "        self.actor_target.load_state_dict(self.actor.state_dict())\n",
        "\n",
        "        self.critic1 = Critic(obs_dim + action_dim).to(self.device)\n",
        "        self.critic_target1 = Critic(obs_dim + action_dim).to(self.device)\n",
        "        self.critic_target1.load_state_dict(self.critic1.state_dict())\n",
        "\n",
        "        self.critic2 = Critic(obs_dim + action_dim).to(self.device)\n",
        "        self.critic_target2 = Critic(obs_dim + action_dim).to(self.device)\n",
        "        self.critic_target2.load_state_dict(self.critic2.state_dict())\n",
        "\n",
        "        # concat critic parameters to use one optim\n",
        "        critic_parameters = list(self.critic1.parameters()) + list(self.critic2.parameters())\n",
        "\n",
        "        # optimizer\n",
        "        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=3e-4)\n",
        "        self.critic_optimizer = optim.Adam(critic_parameters, lr=1e-3)\n",
        "\n",
        "        # transition to store in memory\n",
        "        self.transition = list()\n",
        "\n",
        "        # total steps count\n",
        "        self.total_step = 0\n",
        "\n",
        "        # update step for actor\n",
        "        self.update_step = 0\n",
        "\n",
        "        # mode: train / test\n",
        "        self.is_test = False\n",
        "\n",
        "    def select_action(self, state: np.ndarray) -> np.ndarray:\n",
        "        \"\"\"Select an action from the input state.\"\"\"\n",
        "        # if initial random action should be conducted\n",
        "        if self.total_step < self.initial_random_steps and not self.is_test:\n",
        "            selected_action = self.env.action_space.sample()\n",
        "        else:\n",
        "            selected_action = (\n",
        "                self.actor(torch.FloatTensor(state).to(self.device))[0].detach().cpu().numpy()\n",
        "            )\n",
        "\n",
        "        # add noise for exploration during training\n",
        "        if not self.is_test:\n",
        "            noise = self.exploration_noise.sample()\n",
        "            selected_action = np.clip(selected_action + noise, -1.0, 1.0)\n",
        "\n",
        "            self.transition = [state, selected_action]\n",
        "\n",
        "        return selected_action\n",
        "\n",
        "    def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
        "        \"\"\"Take an action and return the response of the env.\"\"\"\n",
        "        next_state, reward, terminated, truncated, _ = self.env.step(action)\n",
        "        done = terminated or truncated\n",
        "\n",
        "        if not self.is_test:\n",
        "            self.transition += [reward, next_state, done]\n",
        "            self.memory.store(*self.transition)\n",
        "\n",
        "        return next_state, reward, done\n",
        "\n",
        "    def update_model(self) -> torch.Tensor:\n",
        "        \"\"\"Update the model by gradient descent.\"\"\"\n",
        "        device = self.device  # for shortening the following lines\n",
        "\n",
        "        samples = self.memory.sample_batch()\n",
        "        states = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
        "        next_states = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
        "        actions = torch.FloatTensor(samples[\"acts\"]).to(device)\n",
        "        rewards = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
        "        dones = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
        "        masks = 1 - dones\n",
        "\n",
        "        # get actions with noise\n",
        "        noise = torch.FloatTensor(self.target_policy_noise.sample()).to(device)\n",
        "        clipped_noise = torch.clamp(\n",
        "            noise, -self.target_policy_noise_clip, self.target_policy_noise_clip\n",
        "        )\n",
        "\n",
        "        next_actions = (self.actor_target(next_states) + clipped_noise).clamp(-1.0, 1.0)\n",
        "\n",
        "        # min (Q_1', Q_2')\n",
        "        next_values1 = self.critic_target1(next_states, next_actions)\n",
        "        next_values2 = self.critic_target2(next_states, next_actions)\n",
        "        next_values = torch.min(next_values1, next_values2)\n",
        "\n",
        "        # G_t   = r + gamma * v(s_{t+1})  if state != Terminal\n",
        "        #       = r                       otherwise\n",
        "        curr_returns = rewards + self.gamma * next_values * masks\n",
        "        curr_returns = curr_returns.detach()\n",
        "\n",
        "        # critic loss\n",
        "        values1 = self.critic1(states, actions)\n",
        "        values2 = self.critic2(states, actions)\n",
        "        critic1_loss = F.mse_loss(values1, curr_returns)\n",
        "        critic2_loss = F.mse_loss(values2, curr_returns)\n",
        "\n",
        "        # train critic\n",
        "        critic_loss = critic1_loss + critic2_loss\n",
        "        self.critic_optimizer.zero_grad()\n",
        "        critic_loss.backward()\n",
        "        self.critic_optimizer.step()\n",
        "\n",
        "        if self.total_step % self.policy_update_freq == 0:\n",
        "            # train actor\n",
        "            actor_loss = -self.critic1(states, self.actor(states)).mean()\n",
        "\n",
        "            self.actor_optimizer.zero_grad()\n",
        "            actor_loss.backward()\n",
        "            self.actor_optimizer.step()\n",
        "\n",
        "            # target update\n",
        "            self._target_soft_update()\n",
        "        else:\n",
        "            actor_loss = torch.zeros(())\n",
        "\n",
        "        return actor_loss.data, critic_loss.data\n",
        "\n",
        "    def train(self, num_frames: int, plotting_interval: int = 200):\n",
        "        \"\"\"Train the agent.\"\"\"\n",
        "        self.is_test = False\n",
        "\n",
        "        state, _ = self.env.reset(seed=self.seed)\n",
        "        actor_losses = []\n",
        "        critic_losses = []\n",
        "        scores = []\n",
        "        score = 0\n",
        "\n",
        "        for self.total_step in range(1, num_frames + 1):\n",
        "            action = self.select_action(state)\n",
        "            next_state, reward, done = self.step(action)\n",
        "\n",
        "            state = next_state\n",
        "            score += reward\n",
        "\n",
        "            # if episode ends\n",
        "            if done:\n",
        "                state, _ = self.env.reset(seed=self.seed)\n",
        "                scores.append(score)\n",
        "                score = 0\n",
        "\n",
        "            # if training is ready\n",
        "            if len(self.memory) >= self.batch_size and self.total_step > self.initial_random_steps:\n",
        "                actor_loss, critic_loss = self.update_model()\n",
        "                actor_losses.append(actor_loss.cpu().numpy())\n",
        "                critic_losses.append(critic_loss.cpu().numpy())\n",
        "\n",
        "            # plotting\n",
        "            if self.total_step % plotting_interval == 0:\n",
        "                self._plot(self.total_step, scores, actor_losses, critic_losses)\n",
        "\n",
        "        self.env.close()\n",
        "\n",
        "    def test(self, video_folder: str):\n",
        "        \"\"\"Test the agent.\"\"\"\n",
        "        self.is_test = True\n",
        "\n",
        "        tmp_env = self.env\n",
        "        self.env = gym.wrappers.RecordVideo(self.env, video_folder=video_folder)\n",
        "\n",
        "        state, _ = self.env.reset(seed=self.seed)\n",
        "        done = False\n",
        "        score = 0\n",
        "\n",
        "        while not done:\n",
        "            action = self.select_action(state)\n",
        "            next_state, reward, done = self.step(action)\n",
        "\n",
        "            state = next_state\n",
        "            score += reward\n",
        "\n",
        "        print(\"score: \", score)\n",
        "        self.env.close()\n",
        "\n",
        "        self.env = tmp_env\n",
        "\n",
        "    def _target_soft_update(self):\n",
        "        \"\"\"Soft-update: target = tau*local + (1-tau)*target.\"\"\"\n",
        "        tau = self.tau\n",
        "        for t_param, l_param in zip(self.actor_target.parameters(), self.actor.parameters()):\n",
        "            t_param.data.copy_(tau * l_param.data + (1.0 - tau) * t_param.data)\n",
        "\n",
        "        for t_param, l_param in zip(self.critic_target1.parameters(), self.critic1.parameters()):\n",
        "            t_param.data.copy_(tau * l_param.data + (1.0 - tau) * t_param.data)\n",
        "\n",
        "        for t_param, l_param in zip(self.critic_target2.parameters(), self.critic2.parameters()):\n",
        "            t_param.data.copy_(tau * l_param.data + (1.0 - tau) * t_param.data)\n",
        "\n",
        "    def _plot(\n",
        "        self,\n",
        "        frame_idx: int,\n",
        "        scores: List[float],\n",
        "        actor_losses: List[float],\n",
        "        critic_losses: List[float],\n",
        "    ):\n",
        "        \"\"\"Plot the training progresses.\"\"\"\n",
        "        clear_output(True)\n",
        "        plt.figure(figsize=(30, 5))\n",
        "        plt.subplot(131)\n",
        "        plt.title(\"frame %s. score: %s\" % (frame_idx, np.mean(scores[-10:])))\n",
        "        plt.plot(scores)\n",
        "        plt.subplot(132)\n",
        "        plt.title(\"actor_loss\")\n",
        "        plt.plot(actor_losses)\n",
        "        plt.subplot(133)\n",
        "        plt.title(\"critic_loss\")\n",
        "        plt.plot(critic_losses)\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zSZfdaLLPggn"
      },
      "source": [
        "## Environment\n",
        "*ActionNormalizer* is an action wrapper class to normalize the action values ranged in (-1. 1). Thanks to this class, we can make the agent simply select action values within the zero centered range (-1, 1)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "pihKfCMnPggn"
      },
      "outputs": [],
      "source": [
        "class ActionNormalizer(gym.ActionWrapper):\n",
        "    \"\"\"Rescale and relocate the actions.\"\"\"\n",
        "\n",
        "    def action(self, action: np.ndarray) -> np.ndarray:\n",
        "        \"\"\"Change the range (-1, 1) to (low, high).\"\"\"\n",
        "        low = self.action_space.low\n",
        "        high = self.action_space.high\n",
        "\n",
        "        scale_factor = (high - low) / 2\n",
        "        reloc_factor = high - scale_factor\n",
        "\n",
        "        action = action * scale_factor + reloc_factor\n",
        "        action = np.clip(action, low, high)\n",
        "\n",
        "        return action\n",
        "\n",
        "    def reverse_action(self, action: np.ndarray) -> np.ndarray:\n",
        "        \"\"\"Change the range (low, high) to (-1, 1).\"\"\"\n",
        "        low = self.action_space.low\n",
        "        high = self.action_space.high\n",
        "\n",
        "        scale_factor = (high - low) / 2\n",
        "        reloc_factor = high - scale_factor\n",
        "\n",
        "        action = (action - reloc_factor) / scale_factor\n",
        "        action = np.clip(action, -1.0, 1.0)\n",
        "\n",
        "        return action"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SVXyz01CPggn"
      },
      "source": [
        "You can see [the code](https://github.com/Farama-Foundation/Gymnasium/blob/main/gymnasium/envs/classic_control/pendulum.py) and [configurations](https://github.com/Farama-Foundation/Gymnasium/blob/e73245912087d47b538dcdb45fa9a9d185b805c5/gymnasium/envs/__init__.py#L41) of Pendulum-v1 from Gymnasyim repository."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "8Dgiu4DqPggn"
      },
      "outputs": [],
      "source": [
        "# environment\n",
        "env = gym.make(\"Pendulum-v1\", render_mode=\"rgb_array\")\n",
        "env = ActionNormalizer(env)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jp8ncVgqPggn"
      },
      "source": [
        "## Set random seed"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "_AodxUQEPggn"
      },
      "outputs": [],
      "source": [
        "def seed_torch(seed):\n",
        "    torch.manual_seed(seed)\n",
        "    if torch.backends.cudnn.enabled:\n",
        "        torch.backends.cudnn.benchmark = False\n",
        "        torch.backends.cudnn.deterministic = True\n",
        "\n",
        "\n",
        "seed = 777\n",
        "random.seed(seed)\n",
        "np.random.seed(seed)\n",
        "seed_torch(seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WUmq-sFDPggo"
      },
      "source": [
        "## Initialize"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6C2phgU0Pggo",
        "outputId": "b9f1d839-69a2-49e8-b0fb-fa844a6d288b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cuda\n"
          ]
        }
      ],
      "source": [
        "# parameters\n",
        "num_frames = 50000\n",
        "memory_size = 100000\n",
        "batch_size = 128\n",
        "initial_random_steps = 10000\n",
        "\n",
        "agent = TD3Agent(env, memory_size, batch_size, initial_random_steps=initial_random_steps, seed=seed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "swxwhrCVPggo"
      },
      "source": [
        "## Train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "id": "FHLPS9MAPggo",
        "outputId": "feb214c7-cae6-4b97-d5fb-6f7747caf2d9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3000x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "agent.train(num_frames)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U6bFhE6VPggo"
      },
      "source": [
        "## Test\n",
        "Run the trained agent (1 episode)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8WO81xN6Pggo",
        "outputId": "13807e2a-ea5a-43da-dfbb-ebbd85f99f7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gymnasium/wrappers/rendering.py:283: UserWarning: \u001b[33mWARN: Overwriting existing videos at /content/videos/td3 folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
            "  logger.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score:  -125.55866347145377\n"
          ]
        }
      ],
      "source": [
        "# test\n",
        "video_folder = \"videos/td3\"\n",
        "agent.test(video_folder=video_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ViEG3OoAPggo"
      },
      "source": [
        "## Render"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "FnPuWnuqPggo",
        "outputId": "68dac121-d28c-4b92-9413-7a0e7f5126b8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "        <video width=\"320\" height=\"240\" alt=\"test\" controls>\n",
              "        <source src=\"data:video/mp4;base64,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type=\"video/mp4\"/>\n",
              "        </video>\n",
              "        "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Played: videos/td3/rl-video-episode-0.mp4\n"
          ]
        }
      ],
      "source": [
        "import base64\n",
        "import glob\n",
        "import io\n",
        "import os\n",
        "\n",
        "from IPython.display import HTML, display\n",
        "\n",
        "\n",
        "def ipython_show_video(path: str) -> None:\n",
        "    \"\"\"Show a video at `path` within IPython Notebook.\"\"\"\n",
        "    if not os.path.isfile(path):\n",
        "        raise NameError(\"Cannot access: {}\".format(path))\n",
        "\n",
        "    video = io.open(path, \"r+b\").read()\n",
        "    encoded = base64.b64encode(video)\n",
        "\n",
        "    display(\n",
        "        HTML(\n",
        "            data=\"\"\"\n",
        "        <video width=\"320\" height=\"240\" alt=\"test\" controls>\n",
        "        <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\"/>\n",
        "        </video>\n",
        "        \"\"\".format(\n",
        "                encoded.decode(\"ascii\")\n",
        "            )\n",
        "        )\n",
        "    )\n",
        "\n",
        "\n",
        "def show_latest_video(video_folder: str) -> str:\n",
        "    \"\"\"Show the most recently recorded video from video folder.\"\"\"\n",
        "    list_of_files = glob.glob(os.path.join(video_folder, \"*.mp4\"))\n",
        "    latest_file = max(list_of_files, key=os.path.getctime)\n",
        "    ipython_show_video(latest_file)\n",
        "    return latest_file\n",
        "\n",
        "\n",
        "latest_file = show_latest_video(video_folder=video_folder)\n",
        "print(\"Played:\", latest_file)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9CPeCapxPggo"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.13"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}