{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "# Training using REINFORCE for Mujoco\n",
        "\n",
        "<img src=\"file://_static/img/tutorials/reinforce_invpend_gym_v26_fig1.gif\" width=\"400\" alt=\"agent-environment-diagram\">\n",
        "\n",
        "This tutorial serves 2 purposes:\n",
        " 1. To understand how to implement REINFORCE [1] from scratch to solve Mujoco's InvertedPendulum-v4\n",
        " 2. Implementation a deep reinforcement learning algorithm with Gymnasium's v0.26+ `step()` function\n",
        "\n",
        "We will be using **REINFORCE**, one of the earliest policy gradient methods. Unlike going under the burden of learning a value function first and then deriving a policy out of it,\n",
        "REINFORCE optimizes the policy directly. In other words, it is trained to maximize the probability of Monte-Carlo returns. More on that later.\n",
        "\n",
        "**Inverted Pendulum** is Mujoco's cartpole but now powered by the Mujoco physics simulator -\n",
        "which allows more complex experiments (such as varying the effects of gravity).\n",
        "This environment involves a cart that can moved linearly, with a pole fixed on it at one end and having another end free.\n",
        "The cart can be pushed left or right, and the goal is to balance the pole on the top of the cart by applying forces on the cart.\n",
        "More information on the environment could be found at https://gymnasium.farama.org/environments/mujoco/inverted_pendulum/\n",
        "\n",
        "**Training Objectives**: To balance the pole (inverted pendulum) on top of the cart\n",
        "\n",
        "**Actions**: The agent takes a 1D vector for actions. The action space is a continuous ``(action)`` in ``[-3, 3]``,\n",
        "where action represents the numerical force applied to the cart\n",
        "(with magnitude representing the amount of force and sign representing the direction)\n",
        "\n",
        "**Approach**: We use PyTorch to code REINFORCE from scratch to train a Neural Network policy to master Inverted Pendulum.\n",
        "\n",
        "An explanation of the Gymnasium v0.26+ `Env.step()` function\n",
        "\n",
        "``env.step(A)`` allows us to take an action 'A' in the current environment 'env'. The environment then executes the action\n",
        "and returns five variables:\n",
        "\n",
        "-  ``next_obs``: This is the observation that the agent will receive after taking the action.\n",
        "-  ``reward``: This is the reward that the agent will receive after taking the action.\n",
        "-  ``terminated``: This is a boolean variable that indicates whether or not the environment has terminated.\n",
        "-  ``truncated``: This is a boolean variable that also indicates whether the episode ended by early truncation, i.e., a time limit is reached.\n",
        "-  ``info``: This is a dictionary that might contain additional information about the environment.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from __future__ import annotations\n",
        "\n",
        "import random\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.distributions.normal import Normal\n",
        "\n",
        "import gymnasium as gym\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (10, 5)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Policy Network\n",
        "\n",
        "<img src=\"file://_static/img/tutorials/reinforce_invpend_gym_v26_fig2.png\">\n",
        "\n",
        "We start by building a policy that the agent will learn using REINFORCE.\n",
        "A policy is a mapping from the current environment observation to a probability distribution of the actions to be taken.\n",
        "The policy used in the tutorial is parameterized by a neural network. It consists of 2 linear layers that are shared between both the predicted mean and standard deviation.\n",
        "Further, the single individual linear layers are used to estimate the mean and the standard deviation. ``nn.Tanh`` is used as a non-linearity between the hidden layers.\n",
        "The following function estimates a mean and standard deviation of a normal distribution from which an action is sampled. Hence it is expected for the policy to learn\n",
        "appropriate weights to output means and standard deviation based on the current observation.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "class Policy_Network(nn.Module):\n",
        "    \"\"\"Parametrized Policy Network.\"\"\"\n",
        "\n",
        "    def __init__(self, obs_space_dims: int, action_space_dims: int):\n",
        "        \"\"\"Initializes a neural network that estimates the mean and standard deviation\n",
        "         of a normal distribution from which an action is sampled from.\n",
        "\n",
        "        Args:\n",
        "            obs_space_dims: Dimension of the observation space\n",
        "            action_space_dims: Dimension of the action space\n",
        "        \"\"\"\n",
        "        super().__init__()\n",
        "\n",
        "        hidden_space1 = 16  # Nothing special with 16, feel free to change\n",
        "        hidden_space2 = 32  # Nothing special with 32, feel free to change\n",
        "\n",
        "        # Shared Network\n",
        "        self.shared_net = nn.Sequential(\n",
        "            nn.Linear(obs_space_dims, hidden_space1),\n",
        "            nn.Tanh(),\n",
        "            nn.Linear(hidden_space1, hidden_space2),\n",
        "            nn.Tanh(),\n",
        "        )\n",
        "\n",
        "        # Policy Mean specific Linear Layer\n",
        "        self.policy_mean_net = nn.Sequential(\n",
        "            nn.Linear(hidden_space2, action_space_dims)\n",
        "        )\n",
        "\n",
        "        # Policy Std Dev specific Linear Layer\n",
        "        self.policy_stddev_net = nn.Sequential(\n",
        "            nn.Linear(hidden_space2, action_space_dims)\n",
        "        )\n",
        "\n",
        "    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:\n",
        "        \"\"\"Conditioned on the observation, returns the mean and standard deviation\n",
        "         of a normal distribution from which an action is sampled from.\n",
        "\n",
        "        Args:\n",
        "            x: Observation from the environment\n",
        "\n",
        "        Returns:\n",
        "            action_means: predicted mean of the normal distribution\n",
        "            action_stddevs: predicted standard deviation of the normal distribution\n",
        "        \"\"\"\n",
        "        shared_features = self.shared_net(x.float())\n",
        "\n",
        "        action_means = self.policy_mean_net(shared_features)\n",
        "        action_stddevs = torch.log(\n",
        "            1 + torch.exp(self.policy_stddev_net(shared_features))\n",
        "        )\n",
        "\n",
        "        return action_means, action_stddevs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Building an agent\n",
        "\n",
        "<img src=\"file://_static/img/tutorials/reinforce_invpend_gym_v26_fig3.jpeg\">\n",
        "\n",
        "Now that we are done building the policy, let us develop **REINFORCE** which gives life to the policy network.\n",
        "The algorithm of REINFORCE could be found above. As mentioned before, REINFORCE aims to maximize the Monte-Carlo returns.\n",
        "\n",
        "Fun Fact: REINFROCE is an acronym for \" 'RE'ward 'I'ncrement 'N'on-negative 'F'actor times 'O'ffset 'R'einforcement times 'C'haracteristic 'E'ligibility\n",
        "\n",
        "Note: The choice of hyperparameters is to train a decently performing agent. No extensive hyperparameter\n",
        "tuning was done.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "class REINFORCE:\n",
        "    \"\"\"REINFORCE algorithm.\"\"\"\n",
        "\n",
        "    def __init__(self, obs_space_dims: int, action_space_dims: int):\n",
        "        \"\"\"Initializes an agent that learns a policy via REINFORCE algorithm [1]\n",
        "        to solve the task at hand (Inverted Pendulum v4).\n",
        "\n",
        "        Args:\n",
        "            obs_space_dims: Dimension of the observation space\n",
        "            action_space_dims: Dimension of the action space\n",
        "        \"\"\"\n",
        "\n",
        "        # Hyperparameters\n",
        "        self.learning_rate = 1e-4  # Learning rate for policy optimization\n",
        "        self.gamma = 0.99  # Discount factor\n",
        "        self.eps = 1e-6  # small number for mathematical stability\n",
        "\n",
        "        self.probs = []  # Stores probability values of the sampled action\n",
        "        self.rewards = []  # Stores the corresponding rewards\n",
        "\n",
        "        self.net = Policy_Network(obs_space_dims, action_space_dims)\n",
        "        self.optimizer = torch.optim.AdamW(self.net.parameters(), lr=self.learning_rate)\n",
        "\n",
        "    def sample_action(self, state: np.ndarray) -> float:\n",
        "        \"\"\"Returns an action, conditioned on the policy and observation.\n",
        "\n",
        "        Args:\n",
        "            state: Observation from the environment\n",
        "\n",
        "        Returns:\n",
        "            action: Action to be performed\n",
        "        \"\"\"\n",
        "        state = torch.tensor(np.array([state]))\n",
        "        action_means, action_stddevs = self.net(state)\n",
        "\n",
        "        # create a normal distribution from the predicted\n",
        "        #   mean and standard deviation and sample an action\n",
        "        distrib = Normal(action_means[0] + self.eps, action_stddevs[0] + self.eps)\n",
        "        action = distrib.sample()\n",
        "        prob = distrib.log_prob(action)\n",
        "\n",
        "        action = action.numpy()\n",
        "\n",
        "        self.probs.append(prob)\n",
        "\n",
        "        return action\n",
        "\n",
        "    def update(self):\n",
        "        \"\"\"Updates the policy network's weights.\"\"\"\n",
        "        running_g = 0\n",
        "        gs = []\n",
        "\n",
        "        # Discounted return (backwards) - [::-1] will return an array in reverse\n",
        "        for R in self.rewards[::-1]:\n",
        "            running_g = R + self.gamma * running_g\n",
        "            gs.insert(0, running_g)\n",
        "\n",
        "        deltas = torch.tensor(gs)\n",
        "\n",
        "        loss = 0\n",
        "        # minimize -1 * prob * reward obtained\n",
        "        for log_prob, delta in zip(self.probs, deltas):\n",
        "            loss += log_prob.mean() * delta * (-1)\n",
        "\n",
        "        # Update the policy network\n",
        "        self.optimizer.zero_grad()\n",
        "        loss.backward()\n",
        "        self.optimizer.step()\n",
        "\n",
        "        # Empty / zero out all episode-centric/related variables\n",
        "        self.probs = []\n",
        "        self.rewards = []"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now lets train the policy using REINFORCE to master the task of Inverted Pendulum.\n",
        "\n",
        "Following is the overview of the training procedure\n",
        "\n",
        "   for seed in random seeds\n",
        "       reinitialize agent\n",
        "\n",
        "       for episode in range of max number of episodes\n",
        "           until episode is done\n",
        "               sample action based on current observation\n",
        "\n",
        "               take action and receive reward and next observation\n",
        "\n",
        "               store action take, its probability, and the observed reward\n",
        "           update the policy\n",
        "\n",
        "Note: Deep RL is fairly brittle concerning random seed in a lot of common use cases (https://spinningup.openai.com/en/latest/spinningup/spinningup.html).\n",
        "Hence it is important to test out various seeds, which we will be doing.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Episode: 0 Average Reward: 8\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[5], line 28\u001b[0m\n\u001b[1;32m     26\u001b[0m done \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m done:\n\u001b[0;32m---> 28\u001b[0m     action \u001b[38;5;241m=\u001b[39m \u001b[43magent\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msample_action\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     30\u001b[0m     \u001b[38;5;66;03m# Step return type - `tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]`\u001b[39;00m\n\u001b[1;32m     31\u001b[0m     \u001b[38;5;66;03m# These represent the next observation, the reward from the step,\u001b[39;00m\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;66;03m# if the episode is terminated, if the episode is truncated and\u001b[39;00m\n\u001b[1;32m     33\u001b[0m     \u001b[38;5;66;03m# additional info from the step\u001b[39;00m\n\u001b[1;32m     34\u001b[0m     obs, reward, terminated, truncated, info \u001b[38;5;241m=\u001b[39m wrapped_env\u001b[38;5;241m.\u001b[39mstep(action)\n",
            "Cell \u001b[0;32mIn[4], line 40\u001b[0m, in \u001b[0;36mREINFORCE.sample_action\u001b[0;34m(self, state)\u001b[0m\n\u001b[1;32m     38\u001b[0m distrib \u001b[38;5;241m=\u001b[39m Normal(action_means[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps, action_stddevs[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39meps)\n\u001b[1;32m     39\u001b[0m action \u001b[38;5;241m=\u001b[39m distrib\u001b[38;5;241m.\u001b[39msample()\n\u001b[0;32m---> 40\u001b[0m prob \u001b[38;5;241m=\u001b[39m \u001b[43mdistrib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlog_prob\u001b[49m\u001b[43m(\u001b[49m\u001b[43maction\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     42\u001b[0m action \u001b[38;5;241m=\u001b[39m action\u001b[38;5;241m.\u001b[39mnumpy()\n\u001b[1;32m     44\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprobs\u001b[38;5;241m.\u001b[39mappend(prob)\n",
            "File \u001b[0;32m~/anaconda3/envs/pycuda_3_10/lib/python3.10/site-packages/torch/distributions/normal.py:83\u001b[0m, in \u001b[0;36mNormal.log_prob\u001b[0;34m(self, value)\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;66;03m# compute the variance\u001b[39;00m\n\u001b[1;32m     81\u001b[0m var \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m2\u001b[39m\n\u001b[1;32m     82\u001b[0m log_scale \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m---> 83\u001b[0m     math\u001b[38;5;241m.\u001b[39mlog(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43misinstance\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mReal\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale\u001b[38;5;241m.\u001b[39mlog()\n\u001b[1;32m     84\u001b[0m )\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m (\n\u001b[1;32m     86\u001b[0m     \u001b[38;5;241m-\u001b[39m((value \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloc) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;241m2\u001b[39m) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m var)\n\u001b[1;32m     87\u001b[0m     \u001b[38;5;241m-\u001b[39m log_scale\n\u001b[1;32m     88\u001b[0m     \u001b[38;5;241m-\u001b[39m math\u001b[38;5;241m.\u001b[39mlog(math\u001b[38;5;241m.\u001b[39msqrt(\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m math\u001b[38;5;241m.\u001b[39mpi))\n\u001b[1;32m     89\u001b[0m )\n",
            "File \u001b[0;32m~/anaconda3/envs/pycuda_3_10/lib/python3.10/abc.py:117\u001b[0m, in \u001b[0;36mABCMeta.__instancecheck__\u001b[0;34m(cls, instance)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Register a virtual subclass of an ABC.\u001b[39;00m\n\u001b[1;32m    112\u001b[0m \n\u001b[1;32m    113\u001b[0m \u001b[38;5;124;03m    Returns the subclass, to allow usage as a class decorator.\u001b[39;00m\n\u001b[1;32m    114\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m    115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _abc_register(\u001b[38;5;28mcls\u001b[39m, subclass)\n\u001b[0;32m--> 117\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__instancecheck__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, instance):\n\u001b[1;32m    118\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Override for isinstance(instance, cls).\"\"\"\u001b[39;00m\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _abc_instancecheck(\u001b[38;5;28mcls\u001b[39m, instance)\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "# Create and wrap the environment\n",
        "env = gym.make(\"InvertedPendulum-v4\")\n",
        "wrapped_env = gym.wrappers.RecordEpisodeStatistics(env, 50)  # Records episode-reward\n",
        "\n",
        "total_num_episodes = int(5e3)  # Total number of episodes\n",
        "# Observation-space of InvertedPendulum-v4 (4)\n",
        "obs_space_dims = env.observation_space.shape[0]\n",
        "# Action-space of InvertedPendulum-v4 (1)\n",
        "action_space_dims = env.action_space.shape[0]\n",
        "rewards_over_seeds = []\n",
        "\n",
        "for seed in [1, 2, 3, 5, 8]:  # Fibonacci seeds\n",
        "    # set seed\n",
        "    torch.manual_seed(seed)\n",
        "    random.seed(seed)\n",
        "    np.random.seed(seed)\n",
        "\n",
        "    # Reinitialize agent every seed\n",
        "    agent = REINFORCE(obs_space_dims, action_space_dims)\n",
        "    reward_over_episodes = []\n",
        "\n",
        "    for episode in range(total_num_episodes):\n",
        "        # gymnasium v26 requires users to set seed while resetting the environment\n",
        "        obs, info = wrapped_env.reset(seed=seed)\n",
        "\n",
        "        done = False\n",
        "        while not done:\n",
        "            action = agent.sample_action(obs)\n",
        "\n",
        "            # Step return type - `tuple[ObsType, SupportsFloat, bool, bool, dict[str, Any]]`\n",
        "            # These represent the next observation, the reward from the step,\n",
        "            # if the episode is terminated, if the episode is truncated and\n",
        "            # additional info from the step\n",
        "            obs, reward, terminated, truncated, info = wrapped_env.step(action)\n",
        "            agent.rewards.append(reward)\n",
        "\n",
        "            # End the episode when either truncated or terminated is true\n",
        "            #  - truncated: The episode duration reaches max number of timesteps\n",
        "            #  - terminated: Any of the state space values is no longer finite.\n",
        "            done = terminated or truncated\n",
        "\n",
        "        reward_over_episodes.append(wrapped_env.return_queue[-1])\n",
        "        agent.update()\n",
        "\n",
        "        if episode % 1000 == 0:\n",
        "            avg_reward = int(np.mean(wrapped_env.return_queue))\n",
        "            print(\"Episode:\", episode, \"Average Reward:\", avg_reward)\n",
        "\n",
        "    rewards_over_seeds.append(reward_over_episodes)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot learning curve\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "collapsed": false
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/home/frank14f/anaconda3/envs/pycuda_3_10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
            "  with pd.option_context('mode.use_inf_as_na', True):\n",
            "/home/frank14f/anaconda3/envs/pycuda_3_10/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
            "  with pd.option_context('mode.use_inf_as_na', True):\n"
          ]
        },
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "rewards_to_plot = [[reward[0] for reward in rewards] for rewards in rewards_over_seeds]\n",
        "df1 = pd.DataFrame(rewards_to_plot).melt()\n",
        "df1.rename(columns={\"variable\": \"episodes\", \"value\": \"reward\"}, inplace=True)\n",
        "sns.set(style=\"darkgrid\", context=\"talk\", palette=\"rainbow\")\n",
        "sns.lineplot(x=\"episodes\", y=\"reward\", data=df1).set(\n",
        "    title=\"InvertedPendulum\"\n",
        ")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<img src=\"file://_static/img/tutorials/reinforce_invpend_gym_v26_fig4.png\">\n",
        "\n",
        "Author: Siddarth Chandrasekar\n",
        "\n",
        "License: MIT License\n",
        "\n",
        "## References\n",
        "\n",
        "[1] Williams, Ronald J.. “Simple statistical gradient-following\n",
        "algorithms for connectionist reinforcement learning.” Machine Learning 8\n",
        "(2004): 229-256.\n",
        "\n",
        "\n"
      ]
    }
  ],
  "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": "3.10.13"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}