a2c_discrete.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synchronous Advantage Actor-Critic (A2C)\n",
    "\n",
    "This is the A2C algorithm with a discrete action space (only works on environments with discrete actions). Synchronous Advantage Actor-Critic (A2C) is an actor-critic method which runs multiple environments in parallel. A2C is a synchronous version of the asynchronous algorithm A3C. The updates will be performed in a Monte-Carlo way (parameter updates will only be performed at the end of the episode). This is an actor-critic implementation since bootstrapping is used to estimate the return for non-terminal states in addition to the policy gradient used for choosing the actions.\n",
    "\n",
    "The advantage function, $A^{\\pi}$, following policy $\\pi$ is defined by:\n",
    "$$A^{\\pi} = Q^{\\pi}(s_t, a_t) - V^{\\pi}(s_t),$$\n",
    "where $Q^{\\pi}(s_t, a_t)$ is the action-value function following policy $\\pi$ and $V^{\\pi}(s_t)$ is the state-value function following policy $\\pi$. The advantage function essentially measures the advantage gained (or lost) by taking the action $a_t$ in state $s_t$ over the average return of all immediate actions, and then following policy $\\pi$ in the following states. We don't know the absolute value of the advantage, $A^{\\pi}$, hence we need to find an advantage estimate, $\\hat{A}$, as a substitute.\n",
    "\n",
    "The advantage estimate used in A2C/A3C is given by:\n",
    "$$\\hat{A}_{t} = G_t - \\hat{v}(S_t, w),$$ \n",
    "where $G_t$ is the discounted return for terminal episodes or the bootstrapped return for non-terminal episodes, and $\\hat{v}(S_t, w)$ is the value estimate given state $S_t$ and parameters $w$. The bias-variance trade-off can be finetuned by altering the discount rate $\\gamma$. Larger discount/decay rates lead to higher variance and lower bias and lower discount/decay rates lead to lower variance and higher bias. \n",
    "\n",
    "\n",
    "We will be using autograd to perform gradient descent on the policy and value functions. We only need to construct the objective functions (loss functions) and their derivatives will be calculated automatically. We will be using the Adam optimizer for both policy and value gradient updates. The loss functions will be combined into a single loss function. Autograd traces the paths of all the parameters for the gradient calculation, so combining the losses won't impact the gradient calculations. \n",
    "\n",
    "The policy objective function is given by:\n",
    "$$L_{t}^{PF} = -\\hat{E_t}[\\log(\\pi_\\theta(a_t | s_t))\\hat{A}_{t}].$$\n",
    "\n",
    "The value objective function is given by:\n",
    "$$L_{t}^{VF} = \\hat{E_t}[\\hat{A}_{t}^2].$$\n",
    "\n",
    "The final objective function is given by:\n",
    "$$L_{t} = \\hat{E_t}[L_{t}^{PF} + c_{1}L_{t}^{VF} - c_{2}S_{\\pi_{\\theta}}(s_t)],$$\n",
    "where $S_{\\pi_{\\theta}}(s_t)$ is the entropy of the policy function, and $c_{1}$, $c_{2}$ are the objective coefficients. Increasing $c_{1}$ will increase the update size on the value function (the same effect as increasing the learning rate) and increasing $c_{2}$ will make the policy function less deterministic. Decreasing the coefficients will have the opposite effect. \n",
    "\n",
    "\n",
    "For more information on A2C/A3C, see the original A3C paper [1] [Asynchronous Methods for Deep Reinforcement Learning](https://arxiv.org/abs/1602.01783), the notebook [2] [RL-Adventure-2 Actor-Critic Notebook](https://github.com/higgsfield/RL-Adventure-2/blob/master/1.actor-critic.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import gymnasium as gym\n",
    "from IPython.display import Video\n",
    "import jax\n",
    "import flax.linen as nn\n",
    "from jax import value_and_grad\n",
    "import jax.numpy as jnp\n",
    "import optax\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from vec_env.subproc_vec_env import SubprocVecEnv\n",
    "# Print numpy array floats with 2 decimal places\n",
    "np.set_printoptions(precision=2, suppress=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Type of environment, options include:\n",
    "# CartPole-v1, MountainCar-v0, LunarLander-v3, Acrobot-v1\n",
    "ENV_TYPE = \"LunarLander-v3\"\n",
    "# Choose number of parallel environments, \n",
    "# don't exceed the number of computer threads\n",
    "num_envs = 10\n",
    "\n",
    "# Save videos from training\n",
    "SAVE_VIDEOS = True\n",
    "VIDEOS_ROOT_DIR = './videos/a2c_discrete/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_env(env_id):\n",
    "    \"\"\"\n",
    "    Returns a single environment object, \n",
    "    which will be used for parallel execution.\n",
    "    \"\"\"\n",
    "    def _init():\n",
    "        env = gym.make(env_id)\n",
    "        return env\n",
    "    return _init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create multiple envs for parallel execution\n",
    "envs = SubprocVecEnv([make_env(ENV_TYPE) for _ in range(num_envs)])\n",
    "\n",
    "num_actions = envs.action_space.n\n",
    "obs_dims = envs.observation_space.shape\n",
    "obs_upper_bounds = envs.observation_space.high\n",
    "obs_lower_bounds = envs.observation_space.low"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create The Policy "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An NVIDIA GPU may be present on this machine, but a CUDA-enabled jaxlib is not installed. Falling back to cpu.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[3m                               _policy_fn Summary                               \u001b[0m\n",
      "┏━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓\n",
      "┃\u001b[1m \u001b[0m\u001b[1mpath   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mmodule    \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1minputs         \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutputs        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mparams           \u001b[0m\u001b[1m \u001b[0m┃\n",
      "┡━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩\n",
      "│         │ _policy_fn │ \u001b[2mfloat32\u001b[0m[10,8]   │ \u001b[2mfloat32\u001b[0m[10,4]   │                   │\n",
      "├─────────┼────────────┼─────────────────┼─────────────────┼───────────────────┤\n",
      "│ Dense_0 │ Dense      │ \u001b[2mfloat32\u001b[0m[10,8]   │ \u001b[2mfloat32\u001b[0m[10,256] │ bias:             │\n",
      "│         │            │                 │                 │ \u001b[2mfloat32\u001b[0m[256]      │\n",
      "│         │            │                 │                 │ kernel:           │\n",
      "│         │            │                 │                 │ \u001b[2mfloat32\u001b[0m[8,256]    │\n",
      "│         │            │                 │                 │                   │\n",
      "│         │            │                 │                 │ \u001b[1m2,304 \u001b[0m\u001b[1;2m(9.2 KB)\u001b[0m    │\n",
      "├─────────┼────────────┼─────────────────┼─────────────────┼───────────────────┤\n",
      "│ Dense_1 │ Dense      │ \u001b[2mfloat32\u001b[0m[10,256] │ \u001b[2mfloat32\u001b[0m[10,4]   │ bias: \u001b[2mfloat32\u001b[0m[4]  │\n",
      "│         │            │                 │                 │ kernel:           │\n",
      "│         │            │                 │                 │ \u001b[2mfloat32\u001b[0m[256,4]    │\n",
      "│         │            │                 │                 │                   │\n",
      "│         │            │                 │                 │ \u001b[1m1,028 \u001b[0m\u001b[1;2m(4.1 KB)\u001b[0m    │\n",
      "├─────────┼────────────┼─────────────────┼─────────────────┼───────────────────┤\n",
      "│\u001b[1m \u001b[0m\u001b[1m       \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m          \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m               \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m          Total\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m3,332 \u001b[0m\u001b[1;2m(13.3 KB)\u001b[0m\u001b[1m  \u001b[0m\u001b[1m \u001b[0m│\n",
      "└─────────┴────────────┴─────────────────┴─────────────────┴───────────────────┘\n",
      "\u001b[1m                                                                                \u001b[0m\n",
      "\u001b[1m                       Total Parameters: 3,332 \u001b[0m\u001b[1;2m(13.3 KB)\u001b[0m\u001b[1m                        \u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class _policy_fn(nn.Module):\n",
    "    \"\"\"\n",
    "    Policy function is an MLP used to choose the \n",
    "    best possible discrete action given the observations from the state.\n",
    "    Each discrete action is given a probability from 0-1.\n",
    "    The probability distribution can then be used \n",
    "    to stocastically choose the next action.  \n",
    "    \"\"\"\n",
    "    @nn.compact\n",
    "    def __call__(self, x):\n",
    "        x = nn.Dense(256)(x)\n",
    "        x = jax.nn.relu(x)\n",
    "        x = nn.Dense(num_actions)(x)\n",
    "        return jax.nn.softmax(x)\n",
    "\n",
    "# Create the model object\n",
    "policy_fn = _policy_fn()\n",
    "# Display the model details\n",
    "dummy_x = envs.reset()\n",
    "dummy_x = jnp.array(dummy_x)\n",
    "rng_key = jax.random.PRNGKey(64)\n",
    "print(policy_fn.tabulate(rng_key, dummy_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[3m                               _value_fn Summary                                \u001b[0m\n",
      "┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓\n",
      "┃\u001b[1m \u001b[0m\u001b[1mpath   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mmodule   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1minputs         \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1moutputs        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mparams            \u001b[0m\u001b[1m \u001b[0m┃\n",
      "┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩\n",
      "│         │ _value_fn │ \u001b[2mfloat32\u001b[0m[10,8]   │ \u001b[2mfloat32\u001b[0m[10,1]   │                    │\n",
      "├─────────┼───────────┼─────────────────┼─────────────────┼────────────────────┤\n",
      "│ Dense_0 │ Dense     │ \u001b[2mfloat32\u001b[0m[10,8]   │ \u001b[2mfloat32\u001b[0m[10,256] │ bias: \u001b[2mfloat32\u001b[0m[256] │\n",
      "│         │           │                 │                 │ kernel:            │\n",
      "│         │           │                 │                 │ \u001b[2mfloat32\u001b[0m[8,256]     │\n",
      "│         │           │                 │                 │                    │\n",
      "│         │           │                 │                 │ \u001b[1m2,304 \u001b[0m\u001b[1;2m(9.2 KB)\u001b[0m     │\n",
      "├─────────┼───────────┼─────────────────┼─────────────────┼────────────────────┤\n",
      "│ Dense_1 │ Dense     │ \u001b[2mfloat32\u001b[0m[10,256] │ \u001b[2mfloat32\u001b[0m[10,1]   │ bias: \u001b[2mfloat32\u001b[0m[1]   │\n",
      "│         │           │                 │                 │ kernel:            │\n",
      "│         │           │                 │                 │ \u001b[2mfloat32\u001b[0m[256,1]     │\n",
      "│         │           │                 │                 │                    │\n",
      "│         │           │                 │                 │ \u001b[1m257 \u001b[0m\u001b[1;2m(1.0 KB)\u001b[0m       │\n",
      "├─────────┼───────────┼─────────────────┼─────────────────┼────────────────────┤\n",
      "│\u001b[1m \u001b[0m\u001b[1m       \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m         \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m               \u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m          Total\u001b[0m\u001b[1m \u001b[0m│\u001b[1m \u001b[0m\u001b[1m2,561 \u001b[0m\u001b[1;2m(10.2 KB)\u001b[0m\u001b[1m   \u001b[0m\u001b[1m \u001b[0m│\n",
      "└─────────┴───────────┴─────────────────┴─────────────────┴────────────────────┘\n",
      "\u001b[1m                                                                                \u001b[0m\n",
      "\u001b[1m                       Total Parameters: 2,561 \u001b[0m\u001b[1;2m(10.2 KB)\u001b[0m\u001b[1m                        \u001b[0m\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class _value_fn(nn.Module):\n",
    "    \"\"\"\n",
    "    Value function is an MLP used to predict the expected return (value) \n",
    "    given the observations from the state.\n",
    "    The value can then be used for bootstrapping, and/or \n",
    "    calculating the advantage from action chosen in the state. \n",
    "    \"\"\"\n",
    "    @nn.compact\n",
    "    def __call__(self, x):\n",
    "        x = nn.Dense(256)(x)\n",
    "        x = jax.nn.relu(x)\n",
    "        x = nn.Dense(1)(x)\n",
    "        return x\n",
    "\n",
    "# Create the model object\n",
    "value_fn = _value_fn()\n",
    "# Display the model details\n",
    "print(value_fn.tabulate(rng_key, dummy_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_model(policy_variables, env, rng_key):\n",
    "    \"\"\"\n",
    "    Runs a single episode following the policy function, \n",
    "    then returns the episodes rewards and steps.\n",
    "    Args:\n",
    "        policy_variables: Policy functions model parameters.\n",
    "        env: Environment used to interact with.\n",
    "        rng_key: Random number generator key.\n",
    "    Returns:\n",
    "        steps: Number of environment steps.\n",
    "        episode_rewards: List of rewards from the episode.\n",
    "    \"\"\"\n",
    "\n",
    "    terminated, truncated = False, False\n",
    "    episode_rewards = []\n",
    "    steps = 0\n",
    "\n",
    "    state, _ = env.reset()\n",
    "    while not (terminated or truncated):\n",
    "        steps += 1\n",
    "        # Select the action following the parametrized policy\n",
    "        action_dist = policy_fn.apply(policy_variables, state)\n",
    "        rng_key, subkey = jax.random.split(rng_key)\n",
    "        action = jax.random.choice(subkey, jnp.arange(num_actions), p=action_dist)\n",
    "        action = np.array(action)\n",
    "        next_state, reward, terminated, truncated, info = env.step(action)\n",
    "\n",
    "        episode_rewards.append(reward)\n",
    "\n",
    "        state = next_state\n",
    "    episode_rewards = np.array(episode_rewards)\n",
    "    return steps, episode_rewards\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_returns(\n",
    "    gamma, next_value, rewards, done\n",
    "):\n",
    "    \"\"\"\n",
    "    Get discounted returns from rewards for each environment.\n",
    "    Args:\n",
    "        gamma: trajectory discount (scalar).\n",
    "        next_value: value functions bootstrapped return for next state.\n",
    "        rewards: agent rewards of taking actions in the environment.\n",
    "        done: flag list for end of episode, \n",
    "            False indicates terminated, and True not terminated.\n",
    "    Returns:\n",
    "        Array of returns for each environment at each step.\n",
    "    \"\"\"\n",
    "    R = jnp.squeeze(next_value)\n",
    "    returns = []\n",
    "\n",
    "    for t in reversed(range(done.shape[0])):\n",
    "        R = rewards[t] + (gamma * R * done[t])\n",
    "        returns.append(R)\n",
    "\n",
    "    returns = jnp.array(list(reversed(returns)))\n",
    "    return returns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_fn(value_variables, policy_variables, last_envs, last_states, rng_key, gamma, max_steps, mean_cycle_returns, mean_cycle_steps):\n",
    "    \"\"\"\n",
    "    Runs a single update cycle, \n",
    "    then calculates the actor and critic losses, \n",
    "    which are then combined and returned.\n",
    "    The gradients on this loss function are then used \n",
    "    to update the policy and actor functions.\n",
    "    Args:\n",
    "        value_variables: Value functions model parameters.\n",
    "        policy_variables: Policy functions model parameters.\n",
    "        last_envs: List of environments in their previous states.\n",
    "        last_states: List of states from the prior update cycle or beginning of the episode.\n",
    "        rng_key: Random number generator key.\n",
    "        gamma: Discount rate for discounting returns.\n",
    "        max_steps: Limit on the number of steps in an episode.\n",
    "        mean_cycle_returns: List of mean returns for each update cycle per environment.\n",
    "        mean_cycle_steps: List of mean episode steps per update cycle per environment.\n",
    "    Returns:\n",
    "        Combined loss of the value and actor functions.\n",
    "    \"\"\"\n",
    "    terminated, truncated, term_or_trunc = jnp.repeat(False, num_envs), jnp.repeat(False, num_envs), jnp.repeat(False, num_envs)\n",
    "    episode_log_probs = []\n",
    "    episode_values = []\n",
    "    episode_rewards = []\n",
    "    done_flag = []\n",
    "    states = last_states\n",
    "    envs = last_envs\n",
    "    \n",
    "    # Follow the policy until max_steps are reached, then update the parameters\n",
    "    for _ in range(max_steps):\n",
    "        # Select the action following the parametrized policy\n",
    "        action_dists = policy_fn.apply(policy_variables, states)\n",
    "        # Stocastically choose the next action based on the probability distribution\n",
    "        actions = []\n",
    "        for dist in action_dists:\n",
    "            rng_key, subkey = jax.random.split(rng_key)\n",
    "            action = jax.random.choice(subkey, jnp.arange(num_actions), p=dist)\n",
    "            actions.append(action)\n",
    "        actions = np.array(actions)\n",
    "        \n",
    "        next_states, rewards, terminated, truncated, info = envs.step(actions)\n",
    "        # Check if env was terminated or truncated in this step\n",
    "        term_or_trunc = jnp.array([term or trunc for term, trunc in zip(terminated, truncated)])\n",
    "        done_flag.append(term_or_trunc)\n",
    "        rewards = jnp.array(rewards)\n",
    "        episode_rewards.append(rewards)\n",
    "        # Log probabilities are needed for the actor loss\n",
    "        log_probs = jnp.array([jnp.log(dist[action_index]) for dist, action_index in zip(action_dists, actions)])\n",
    "        episode_log_probs.append(log_probs)\n",
    "        # Entropy is used in the loss function to prevent the model from becoming too deterministic\n",
    "        entropy = jnp.array([-jnp.sum(dist * jnp.log(dist)) for dist in action_dists])\n",
    "        # Estimate the values given the states, then set all terminated states value to 0\n",
    "        values = value_fn.apply(value_variables, states)\n",
    "        values = jnp.squeeze(values)\n",
    "        values = jnp.array(values, dtype=jnp.float32)\n",
    "        episode_values.append(values)\n",
    "\n",
    "        states = next_states\n",
    "\n",
    "    # Save the last envs/states for the next update cycle\n",
    "    last_states = states\n",
    "    last_envs = envs\n",
    "    # Calculate the mean returns for each environment\n",
    "    done_flag = np.array(done_flag)\n",
    "    num_episodes = (done_flag.sum(axis=0) + 1)\n",
    "    episode_rewards = np.array(episode_rewards)\n",
    "    sum_rewards = episode_rewards.sum(axis=0)\n",
    "    mean_returns = sum_rewards / num_episodes\n",
    "    mean_cycle_returns.append(mean_returns)\n",
    "    # Calculate mean episode steps per cycle \n",
    "    cycle_steps = max_steps / num_episodes\n",
    "    mean_cycle_steps.append(cycle_steps)\n",
    "    done_flag = jnp.invert(done_flag)\n",
    "\n",
    "    # Calculate the advantages\n",
    "    next_values = value_fn.apply(value_variables, next_states)\n",
    "    next_values = jnp.squeeze(next_values)\n",
    "    returns = get_returns(gamma, next_values, episode_rewards, done_flag)\n",
    "    episode_values = jnp.array(episode_values)\n",
    "    advantages = returns - episode_values\n",
    "    # Calculate actor and critic losses, then subtract the entropy \n",
    "    episode_log_probs = jnp.array(episode_log_probs)\n",
    "    actor_loss = -(advantages * episode_log_probs).mean(axis=0)\n",
    "    critic_loss = (advantages**2).mean(axis=0)\n",
    "    loss = (actor_loss + 0.5 * critic_loss - 0.001 * entropy).mean()\n",
    "\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn The Optimal Action-Value Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu22/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/gymnasium/wrappers/rendering.py:282: UserWarning: \u001b[33mWARN: Overwriting existing videos at /home/ubuntu22/repos/reinforcement-learning-algorithms/videos/a2c_discrete folder (try specifying a different `video_folder` for the `RecordVideo` wrapper if this is not desired)\u001b[0m\n",
      "  logger.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Update Step: 0\n",
      "\tTest Episode 0 Return: -341.76 Steps: 114.00\n",
      "\tAverage Cycle Returns: [-142.3   -73.71  -80.22 -118.3   -95.3   -43.67 -194.01  -81.5  -160.18\n",
      " -115.86]\n",
      "\tAverage Return: -110.51\n",
      "\tLoss: 4218.31\n",
      "Update Step: 1\n",
      "\tAverage Cycle Returns: [ -50.21 -185.95 -150.82 -121.19 -225.21  -95.8  -190.91 -129.21  -58.61\n",
      " -114.12]\n",
      "\tAverage Return: -132.20\n",
      "\tLoss: 6770.71\n",
      "Update Step: 2\n",
      "\tAverage Cycle Returns: [-150.5  -155.06  -69.22 -164.67  -97.3   -63.76 -152.16 -148.88  -63.39\n",
      " -144.75]\n",
      "\tAverage Return: -120.97\n",
      "\tLoss: 5426.13\n",
      "Update Step: 3\n",
      "\tAverage Cycle Returns: [ -75.1  -172.51 -270.01 -164.81  -75.36 -117.44  -49.74 -149.24  -69.44\n",
      " -101.2 ]\n",
      "\tAverage Return: -124.49\n",
      "\tLoss: 4189.30\n",
      "Update Step: 4\n",
      "\tTest Episode 1 Return: -3.00 Steps: 88.00\n",
      "\tAverage Cycle Returns: [ -66.44 -272.82 -159.44  -59.28 -168.91 -220.34 -111.61 -120.94  -45.04\n",
      "  -99.85]\n",
      "\tAverage Return: -132.47\n",
      "\tLoss: 6101.93\n",
      "Update Step: 5\n",
      "\tAverage Cycle Returns: [ -84.63 -267.53  -94.34  -77.38  -74.6  -126.27 -111.2  -191.86 -193.83\n",
      " -121.64]\n",
      "\tAverage Return: -134.33\n",
      "\tLoss: 4729.26\n",
      "Update Step: 6\n",
      "\tAverage Cycle Returns: [-156.61 -187.9   -61.38 -151.83 -233.05 -174.26 -299.11 -182.66 -166.47\n",
      "  -44.16]\n",
      "\tAverage Return: -165.74\n",
      "\tLoss: 9889.32\n",
      "Update Step: 7\n",
      "\tAverage Cycle Returns: [-203.59 -102.76  -69.03  -75.47  -73.57 -171.3  -193.2   -97.2  -131.81\n",
      " -305.83]\n",
      "\tAverage Return: -142.38\n",
      "\tLoss: 5014.56\n",
      "Update Step: 8\n",
      "\tTest Episode 2 Return: -200.57 Steps: 72.00\n",
      "\tAverage Cycle Returns: [-119.9   -45.16 -101.52  -20.14  -89.48 -194.58  -82.95 -221.45 -129.18\n",
      " -152.39]\n",
      "\tAverage Return: -115.67\n",
      "\tLoss: 3681.14\n",
      "Update Step: 9\n",
      "\tAverage Cycle Returns: [ -56.01 -134.54 -120.68  -68.07 -317.55 -155.22  -77.98 -167.65 -140.09\n",
      " -190.85]\n",
      "\tAverage Return: -142.86\n",
      "\tLoss: 7691.67\n",
      "Update Step: 10\n",
      "\tAverage Cycle Returns: [ -85.92 -144.98 -160.73  -68.87 -105.56 -123.33 -326.74  -70.53 -104.86\n",
      " -135.95]\n",
      "\tAverage Return: -132.75\n",
      "\tLoss: 5624.47\n",
      "Update Step: 11\n",
      "\tAverage Cycle Returns: [ -97.5  -304.92 -211.53 -116.87  -79.84 -122.87 -177.91  -87.52  -56.09\n",
      " -105.33]\n",
      "\tAverage Return: -136.04\n",
      "\tLoss: 5525.74\n",
      "Update Step: 12\n",
      "\tTest Episode 3 Return: -314.16 Steps: 106.00\n",
      "\tAverage Cycle Returns: [-115.26 -239.48 -159.48  -84.66  -72.35 -194.17 -293.35  -94.33 -214.62\n",
      "  -70.84]\n",
      "\tAverage Return: -153.86\n",
      "\tLoss: 8730.83\n",
      "Update Step: 13\n",
      "\tAverage Cycle Returns: [-196.55 -230.78 -190.51 -219.19 -215.52  -96.88 -182.67  -47.06  -92.09\n",
      "  -87.  ]\n",
      "\tAverage Return: -155.82\n",
      "\tLoss: 8718.22\n",
      "Update Step: 14\n",
      "\tAverage Cycle Returns: [-294.69  -67.39 -217.33 -138.64 -266.31 -181.18 -256.21 -150.7  -146.06\n",
      " -241.27]\n",
      "\tAverage Return: -195.98\n",
      "\tLoss: 10402.14\n",
      "Update Step: 15\n",
      "\tAverage Cycle Returns: [-292.54  -11.65 -221.1   -68.74 -141.96  -94.26  -89.79 -119.3  -125.36\n",
      " -140.64]\n",
      "\tAverage Return: -130.53\n",
      "\tLoss: 4385.02\n",
      "Update Step: 16\n",
      "\tTest Episode 4 Return: -286.47 Steps: 145.00\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[10], line 57\u001b[0m\n\u001b[1;32m     53\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124mTest Episode \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_episode\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Return: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_returns[test_episode]\u001b[38;5;250m \u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Steps: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtest_steps[test_episode]\u001b[38;5;250m \u001b[39m\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     55\u001b[0m \u001b[38;5;66;03m# Run a forward pass on the parallel envs (will last an entire episode) and \u001b[39;00m\n\u001b[1;32m     56\u001b[0m \u001b[38;5;66;03m# calculate the gradients for value and policy functions \u001b[39;00m\n\u001b[0;32m---> 57\u001b[0m loss, (value_grads, policy_grads) \u001b[38;5;241m=\u001b[39m \u001b[43mvalue_and_grad\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss_fn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margnums\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     58\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalue_variables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     59\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpolicy_variables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     60\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlast_envs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     61\u001b[0m \u001b[43m    \u001b[49m\u001b[43mlast_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     62\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrng_key\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     63\u001b[0m \u001b[43m    \u001b[49m\u001b[43mgamma\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     64\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_steps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     65\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmean_cycle_returns\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmean_cycle_steps\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Update value function weights\u001b[39;00m\n\u001b[1;32m     69\u001b[0m value_updates, value_opt_state \u001b[38;5;241m=\u001b[39m value_optimizer\u001b[38;5;241m.\u001b[39mupdate(value_grads, value_opt_state, value_variables)\n",
      "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/api.py:709\u001b[0m, in \u001b[0;36mvalue_and_grad.<locals>.value_and_grad_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    707\u001b[0m _check_scalar(ans)\n\u001b[1;32m    708\u001b[0m tree_map(partial(_check_output_dtype_grad, holomorphic), ans)\n\u001b[0;32m--> 709\u001b[0m g \u001b[38;5;241m=\u001b[39m \u001b[43mvjp_py\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlax_internal\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_one\u001b[49m\u001b[43m(\u001b[49m\u001b[43mans\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    710\u001b[0m g \u001b[38;5;241m=\u001b[39m g[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(argnums, \u001b[38;5;28mint\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m g\n\u001b[1;32m    711\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m has_aux:\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/tree_util.py:477\u001b[0m, in \u001b[0;36m_HashableCallableShim.__call__\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m--> 477\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/api.py:2121\u001b[0m, in \u001b[0;36m_vjp_pullback_wrapper\u001b[0;34m(name, out_primal_avals, io_tree, fun, *py_args_)\u001b[0m\n\u001b[1;32m   2114\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;129;01mnot\u001b[39;00m core\u001b[38;5;241m.\u001b[39mtypecompat(ct_aval, ct_aval_expected) \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[1;32m   2115\u001b[0m       \u001b[38;5;129;01mnot\u001b[39;00m _temporary_dtype_exception(ct_aval, ct_aval_expected)):\n\u001b[1;32m   2116\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m   2117\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124munexpected JAX type (e.g. shape/dtype) for argument to vjp function: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2118\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgot \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mct_aval\u001b[38;5;241m.\u001b[39mstr_short()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m, but expected \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mct_aval_expected\u001b[38;5;241m.\u001b[39mstr_short()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2119\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbecause the corresponding output of the function \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m had JAX type \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2120\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maval\u001b[38;5;241m.\u001b[39mstr_short()\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m-> 2121\u001b[0m ans \u001b[38;5;241m=\u001b[39m \u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m tree_unflatten(out_tree, ans)\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/tree_util.py:477\u001b[0m, in \u001b[0;36m_HashableCallableShim.__call__\u001b[0;34m(self, *args, **kw)\u001b[0m\n\u001b[1;32m    476\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkw):\n\u001b[0;32m--> 477\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkw\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/interpreters/ad.py:149\u001b[0m, in \u001b[0;36mvjp.<locals>.unbound_vjp\u001b[0;34m(pvals, jaxpr, consts, *cts)\u001b[0m\n\u001b[1;32m    147\u001b[0m cts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(ct \u001b[38;5;28;01mfor\u001b[39;00m ct, pval \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(cts, pvals) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m pval\u001b[38;5;241m.\u001b[39mis_known())\n\u001b[1;32m    148\u001b[0m dummy_args \u001b[38;5;241m=\u001b[39m [UndefinedPrimal(v\u001b[38;5;241m.\u001b[39maval) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m jaxpr\u001b[38;5;241m.\u001b[39minvars]\n\u001b[0;32m--> 149\u001b[0m arg_cts \u001b[38;5;241m=\u001b[39m \u001b[43mbackward_pass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjaxpr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconsts\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdummy_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    150\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mmap\u001b[39m(instantiate_zeros, arg_cts)\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/interpreters/ad.py:252\u001b[0m, in \u001b[0;36mbackward_pass\u001b[0;34m(jaxpr, transform_stack, consts, primals_in, cotangents_in)\u001b[0m\n\u001b[1;32m    249\u001b[0m   cts_out \u001b[38;5;241m=\u001b[39m reducing_transposes[eqn\u001b[38;5;241m.\u001b[39mprimitive](\n\u001b[1;32m    250\u001b[0m       cts_in, \u001b[38;5;241m*\u001b[39minvals, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39meqn\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    251\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 252\u001b[0m   cts_out \u001b[38;5;241m=\u001b[39m \u001b[43mget_primitive_transpose\u001b[49m\u001b[43m(\u001b[49m\u001b[43meqn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprimitive\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    253\u001b[0m \u001b[43m      \u001b[49m\u001b[43mcts_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minvals\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43meqn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    254\u001b[0m cts_out \u001b[38;5;241m=\u001b[39m [Zero(v\u001b[38;5;241m.\u001b[39maval) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m eqn\u001b[38;5;241m.\u001b[39minvars] \u001b[38;5;28;01mif\u001b[39;00m cts_out \u001b[38;5;129;01mis\u001b[39;00m Zero \u001b[38;5;28;01melse\u001b[39;00m cts_out\n\u001b[1;32m    255\u001b[0m \u001b[38;5;66;03m# FIXME: Some invars correspond to primals!\u001b[39;00m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/interpreters/ad.py:523\u001b[0m, in \u001b[0;36mlinear_transpose2\u001b[0;34m(transpose_rule, cotangent, *args, **kwargs)\u001b[0m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlinear_transpose2\u001b[39m(transpose_rule, cotangent, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m--> 523\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m Zero \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(cotangent) \u001b[38;5;129;01mis\u001b[39;00m Zero \u001b[38;5;28;01melse\u001b[39;00m \u001b[43mtranspose_rule\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcotangent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/jax/_src/lax/slicing.py:1118\u001b[0m, in \u001b[0;36m_slice_transpose_rule\u001b[0;34m(t, operand, start_indices, limit_indices, strides)\u001b[0m\n\u001b[1;32m   1116\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m ad\u001b[38;5;241m.\u001b[39mis_undefined_primal(operand)\n\u001b[1;32m   1117\u001b[0m operand_shape \u001b[38;5;241m=\u001b[39m operand\u001b[38;5;241m.\u001b[39maval\u001b[38;5;241m.\u001b[39mshape\n\u001b[0;32m-> 1118\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m strides \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mequal\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstrides\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m):\n\u001b[1;32m   1119\u001b[0m   pads \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(start_indices, np\u001b[38;5;241m.\u001b[39msubtract(operand_shape, limit_indices),\n\u001b[1;32m   1120\u001b[0m              (\u001b[38;5;241m0\u001b[39m,) \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mlen\u001b[39m(start_indices))\n\u001b[1;32m   1121\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# Number of cycles to train on\n",
    "update_cycles = 20\n",
    "# Sliding window for update cycle evaluation (needs to be less than num cycles)\n",
    "cycle_window = 5\n",
    "# Episode will terminate before max_steps\n",
    "max_steps = 200\n",
    "# Discount ratio\n",
    "gamma = 0.99\n",
    "# Step size\n",
    "policy_alpha = 0.001\n",
    "value_alpha = 0.001\n",
    "\n",
    "policy_variables = policy_fn.init(rng_key, dummy_x)\n",
    "policy_optimizer = optax.adam(learning_rate=policy_alpha)\n",
    "policy_opt_state = policy_optimizer.init(policy_variables)\n",
    "\n",
    "value_variables = value_fn.init(rng_key, dummy_x)\n",
    "value_optimizer = optax.adam(learning_rate=value_alpha)\n",
    "value_opt_state = value_optimizer.init(value_variables)\n",
    "\n",
    "# Create multiple envs for parallel execution\n",
    "envs = SubprocVecEnv([make_env(ENV_TYPE) for _ in range(num_envs)])\n",
    "if SAVE_VIDEOS:\n",
    "    # Create a single environment for testing\n",
    "    os.makedirs(VIDEOS_ROOT_DIR, exist_ok=True)\n",
    "    test_returns = []\n",
    "    test_steps = []\n",
    "    env = gym.make(ENV_TYPE, render_mode=\"rgb_array\")\n",
    "    env = gym.wrappers.RecordVideo(\n",
    "        env=env, \n",
    "        video_folder=VIDEOS_ROOT_DIR,\n",
    "        episode_trigger=lambda episode: episode % 5 == 0,\n",
    "        video_length=0,\n",
    "        name_prefix=f\"{ENV_TYPE}\",\n",
    "        fps=10\n",
    "        ) \n",
    "losses = []\n",
    "mean_cycle_returns = []\n",
    "mean_cycle_steps = []\n",
    "\n",
    "states = envs.reset()\n",
    "last_states = jnp.array(states)\n",
    "last_envs = envs\n",
    "\n",
    "for update_step in range(update_cycles):\n",
    "    print(f\"Update Step: {update_step}\")\n",
    "    # Perform 6 tests and record them at even intervals\n",
    "    if SAVE_VIDEOS and update_step % (update_cycles // 5) == 0:\n",
    "        test_step, test_rewards = test_model(policy_variables, env, rng_key)\n",
    "        test_returns.append(np.sum(test_rewards))\n",
    "        test_steps.append(test_step)\n",
    "        test_episode = update_step // (update_cycles // 5)\n",
    "        print(f\"\\tTest Episode {test_episode} Return: {test_returns[test_episode] :.2f} Steps: {test_steps[test_episode] :.2f}\")\n",
    "\n",
    "    # Run a forward pass on the parallel envs (will last an entire episode) and \n",
    "    # calculate the gradients for value and policy functions \n",
    "    loss, (value_grads, policy_grads) = value_and_grad(loss_fn, argnums=(0, 1))(\n",
    "        value_variables, \n",
    "        policy_variables, \n",
    "        last_envs,\n",
    "        last_states,\n",
    "        rng_key, \n",
    "        gamma, \n",
    "        max_steps,\n",
    "        mean_cycle_returns,\n",
    "        mean_cycle_steps\n",
    "    )\n",
    "    # Update value function weights\n",
    "    value_updates, value_opt_state = value_optimizer.update(value_grads, value_opt_state, value_variables)\n",
    "    value_variables = optax.apply_updates(value_variables, value_updates)        \n",
    "    # Update policy function weights\n",
    "    policy_updates, policy_opt_state = policy_optimizer.update(policy_grads, policy_opt_state, policy_variables)\n",
    "    policy_variables = optax.apply_updates(policy_variables, policy_updates)\n",
    "\n",
    "\n",
    "    losses.append(loss)\n",
    "    print(f'\\tAverage Cycle Returns: {mean_cycle_returns[-1]}')\n",
    "    print(f\"\\tAverage Return: {mean_cycle_returns[-1].mean() :.2f}\")\n",
    "    print(f\"\\tLoss: {loss :.2f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot The Results\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=1, figsize=(6, 5))\n",
    "axs.set_title(\"Loss\")\n",
    "loss_moving_average = (\n",
    "    np.convolve(\n",
    "        losses, np.ones(cycle_window), mode=\"valid\"\n",
    "    )\n",
    "    / cycle_window\n",
    ")\n",
    "axs.plot(range(len(loss_moving_average)), loss_moving_average)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code used from: https://gymnasium.farama.org/tutorials/blackjack_tutorial/#visualizing-the-training\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "axs[0].set_title(\"Cycle Rewards\")\n",
    "reward_moving_average = (\n",
    "    np.convolve(\n",
    "        np.array(mean_cycle_returns).mean(axis=1), np.ones(cycle_window), mode=\"valid\"\n",
    "    )\n",
    "    / cycle_window\n",
    ")\n",
    "axs[0].plot(range(len(reward_moving_average)), reward_moving_average)\n",
    "axs[1].set_title(\"Cycle Lengths\")\n",
    "length_moving_average = (\n",
    "    np.convolve(\n",
    "        np.array(mean_cycle_steps).mean(axis=1), np.ones(cycle_window), mode=\"valid\"\n",
    "    )\n",
    "    / cycle_window\n",
    ")\n",
    "axs[1].plot(range(len(length_moving_average)), length_moving_average)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(12, 5))\n",
    "axs[0].set_title(\"Test Returns\")\n",
    "axs[0].plot(range(len(test_returns)), test_returns)\n",
    "axs[1].set_title(\"Test Lengths\")\n",
    "axs[1].plot(range(len(test_steps)), test_steps)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing Videos"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 0 Return: -246.09028033649366\n"
     ]
    }
   ],
   "source": [
    "test_episode = 0\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/a2c_discrete//LunarLander-v3-episode-0.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 1 Return: -288.81732784980983\n"
     ]
    }
   ],
   "source": [
    "test_episode = 1\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/a2c_discrete//LunarLander-v3-episode-1.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 2 Return: -68.23192468014639\n"
     ]
    }
   ],
   "source": [
    "test_episode = 2\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/a2c_discrete//LunarLander-v3-episode-2.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 3 Return: -136.07894928542373\n"
     ]
    }
   ],
   "source": [
    "test_episode = 3\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/a2c_discrete//LunarLander-v3-episode-3.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 4 Return: -132.8846224127737\n"
     ]
    }
   ],
   "source": [
    "test_episode = 4\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"./videos/a2c_discrete//LunarLander-v3-episode-4.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
      ],
      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Episode 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Episode 5 Return: -261.78169954618147\n"
     ]
    }
   ],
   "source": [
    "test_episode = 5\n",
    "print(f\"Test Episode {test_episode} Return: {test_returns[test_episode]}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "To embed videos, you must pass embed=True (this may make your notebook files huge)\nConsider passing Video(url='...')",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[26], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mVideo\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mVIDEOS_ROOT_DIR\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m/\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mENV_TYPE\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m-episode-\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mtest_episode\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m.mp4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/repos/reinforcement-learning-algorithms/venv-pre/lib/python3.10/site-packages/IPython/core/display.py:1162\u001b[0m, in \u001b[0;36mVideo.__init__\u001b[0;34m(self, data, url, filename, embed, mimetype, width, height, html_attributes)\u001b[0m\n\u001b[1;32m   1156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m embed:\n\u001b[1;32m   1157\u001b[0m     msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin([\n\u001b[1;32m   1158\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTo embed videos, you must pass embed=True \u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1159\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m(this may make your notebook files huge)\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1160\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConsider passing Video(url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m...\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   1161\u001b[0m     ])\n\u001b[0;32m-> 1162\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n\u001b[1;32m   1164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmimetype \u001b[38;5;241m=\u001b[39m mimetype\n\u001b[1;32m   1165\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membed \u001b[38;5;241m=\u001b[39m embed\n",
      "\u001b[0;31mValueError\u001b[0m: To embed videos, you must pass embed=True (this may make your notebook files huge)\nConsider passing Video(url='...')"
     ]
    }
   ],
   "source": [
    "Video(f\"{VIDEOS_ROOT_DIR}/{ENV_TYPE}-episode-{test_episode}.mp4\")"
   ]
  }
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