From 61da605113e4cce0c356632a53668d965e943ae6 Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Mon, 2 Mar 2026 12:18:55 +0100 Subject: [PATCH] Refactor code structure for improved readability and maintainability --- .../project/Project.ipynb | 1565 ++++++++++++----- pyproject.toml | 34 +- uv.lock | 1275 ++++++-------- 3 files changed, 1691 insertions(+), 1183 deletions(-) diff --git a/M2/Reinforcement Learning/project/Project.ipynb b/M2/Reinforcement Learning/project/Project.ipynb index d964d19..c225b6c 100644 --- a/M2/Reinforcement Learning/project/Project.ipynb +++ b/M2/Reinforcement Learning/project/Project.ipynb @@ -1,56 +1,177 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "fef45687", + "metadata": {}, + "source": [ + "# RL Project: Atari Tennis Tournament (NumPy Agents)\n", + "\n", + "This notebook implements four Reinforcement Learning algorithms **without PyTorch** to play Atari Tennis (`ALE/Tennis-v5` via Gymnasium):\n", + "\n", + "1. **SARSA** — Semi-gradient SARSA with linear approximation (inspired by Lab 7, on-policy update from Lab 5B)\n", + "2. **Q-Learning** — Off-policy linear approximation (inspired by Lab 5B)\n", + "3. **DQN** — Deep Q-Network with pure numpy MLP, experience replay and target network (inspired by Lab 6A + classic DQN)\n", + "4. **Monte Carlo** — First-visit MC control with linear approximation (inspired by Lab 4)\n", + "\n", + "Each agent is **pre-trained independently** against the built-in Atari AI opponent, then evaluated in a comparative tournament." + ] + }, { "cell_type": "code", - "execution_count": 1, - "id": "7e37429a", + "execution_count": 17, + "id": "b50d7174", "metadata": {}, "outputs": [], "source": [ - "import itertools\n", "import pickle\n", - "from collections.abc import Iterator\n", "from pathlib import Path\n", - "from typing import Protocol\n", "\n", - "import supersuit as ss\n", - "from pettingzoo.atari import tennis_v3\n", + "import ale_py # noqa: F401 — registers ALE environments\n", + "import gymnasium as gym\n", + "from gymnasium.wrappers import FrameStackObservation, ResizeObservation\n", + "from tqdm.auto import tqdm\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", - "import seaborn as sns\n", - "\n", - "import torch\n", - "from torch import nn, optim\n" + "import seaborn as sns\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "ff3486a4", + "metadata": {}, + "outputs": [], + "source": [ + "CHECKPOINT_DIR = Path(\"checkpoints\")\n", + "CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)\n" ] }, { "cell_type": "markdown", - "id": "5f888489", + "id": "ec691487", "metadata": {}, "source": [ - "# Agent Definitions\n", + "# Utility Functions\n", "\n", - "## Base Agent Class" + "## Observation Normalization\n", + "\n", + "The Tennis environment produces image observations of shape `(4, 84, 84)` after preprocessing (grayscale + resize + frame stack).\n", + "We normalize them into 1D `float64` vectors divided by 255, as in Lab 7 (continuous feature normalization).\n", + "\n", + "## ε-greedy Policy\n", + "\n", + "Follows the pattern from Lab 5B (`epsilon_greedy`) and Lab 7 (`epsilon_greedy_action`):\n", + "- With probability ε: random action (exploration)\n", + "- With probability 1−ε: action maximizing $\\hat{q}(s, a)$ with uniform tie-breaking (`np.flatnonzero`)" ] }, { "cell_type": "code", - "execution_count": 2, - "id": "85ff0eb4", + "execution_count": 19, + "id": "be85c130", + "metadata": {}, + "outputs": [], + "source": [ + "def normalize_obs(observation: np.ndarray) -> np.ndarray:\n", + " \"\"\"Flatten and normalize an observation to a 1D float64 vector.\n", + "\n", + " Replicates the /255.0 normalization used in all agents from the original project.\n", + " For image observations of shape (4, 84, 84), this produces a vector of length 28_224.\n", + "\n", + " Args:\n", + " observation: Raw observation array from the environment.\n", + "\n", + " Returns:\n", + " 1D numpy array of dtype float64, values in [0, 1].\n", + "\n", + " \"\"\"\n", + " return observation.flatten().astype(np.float64) / 255.0\n", + "\n", + "\n", + "def epsilon_greedy(\n", + " q_values: np.ndarray,\n", + " epsilon: float,\n", + " rng: np.random.Generator,\n", + ") -> int:\n", + " \"\"\"Select an action using an ε-greedy policy with fair tie-breaking.\n", + "\n", + " Follows the same logic as Lab 5B epsilon_greedy and Lab 7 epsilon_greedy_action:\n", + " - With probability epsilon: choose a random action (exploration).\n", + " - With probability 1-epsilon: choose the action with highest Q-value (exploitation).\n", + " - If multiple actions share the maximum Q-value, break ties uniformly at random.\n", + "\n", + " Handles edge cases: empty q_values, NaN/Inf values.\n", + "\n", + " Args:\n", + " q_values: Array of Q-values for each action, shape (n_actions,).\n", + " epsilon: Exploration probability in [0, 1].\n", + " rng: NumPy random number generator.\n", + "\n", + " Returns:\n", + " Selected action index.\n", + "\n", + " \"\"\"\n", + " q_values = np.asarray(q_values, dtype=np.float64).reshape(-1)\n", + "\n", + " if q_values.size == 0:\n", + " msg = \"q_values is empty.\"\n", + " raise ValueError(msg)\n", + "\n", + " if rng.random() < epsilon:\n", + " return int(rng.integers(0, q_values.size))\n", + "\n", + " # Handle NaN/Inf values safely\n", + " finite_mask = np.isfinite(q_values)\n", + " if not np.any(finite_mask):\n", + " return int(rng.integers(0, q_values.size))\n", + "\n", + " safe_q = q_values.copy()\n", + " safe_q[~finite_mask] = -np.inf\n", + " max_val = np.max(safe_q)\n", + " best = np.flatnonzero(safe_q == max_val)\n", + "\n", + " if best.size == 0:\n", + " return int(rng.integers(0, q_values.size))\n", + "\n", + " return int(rng.choice(best))\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb53da28", + "metadata": {}, + "source": [ + "# Agent Definitions\n", + "\n", + "## Base Class `Agent`\n", + "\n", + "Common interface for all agents, same signatures: `get_action`, `update`, `save`, `load`.\n", + "Serialization uses `pickle` (compatible with numpy arrays)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "ded9b1fb", "metadata": {}, "outputs": [], "source": [ "class Agent:\n", - " \"\"\"Base class for reinforcement learning agents.\"\"\"\n", + " \"\"\"Base class for reinforcement learning agents.\n", + "\n", + " All agents share this interface so they are compatible with the tournament system.\n", + " \"\"\"\n", "\n", " def __init__(self, seed: int, action_space: int) -> None:\n", - " \"\"\"Initialize the agent with its action space.\"\"\"\n", + " \"\"\"Initialize the agent with its action space and a reproducible RNG.\"\"\"\n", " self.action_space = action_space\n", - " self.rng = np.random.default_rng(seed = seed)\n", + " self.rng = np.random.default_rng(seed=seed)\n", "\n", - " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> None:\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", " \"\"\"Select an action from the current observation.\"\"\"\n", + " raise NotImplementedError\n", "\n", " def update(\n", " self,\n", @@ -64,7 +185,7 @@ " \"\"\"Update agent parameters from one transition.\"\"\"\n", "\n", " def save(self, filename: str) -> None:\n", - " \"\"\"Save the agent state to disk.\"\"\"\n", + " \"\"\"Save the agent state to disk using pickle.\"\"\"\n", " with Path(filename).open(\"wb\") as f:\n", " pickle.dump(self.__dict__, f)\n", "\n", @@ -76,159 +197,116 @@ }, { "cell_type": "markdown", - "id": "2459be52", + "id": "8a4eae79", "metadata": {}, "source": [ - "## Random Agent" + "## Random Agent (baseline)\n", + "\n", + "Serves as a reference to evaluate the performance of learning agents." ] }, { "cell_type": "code", - "execution_count": 3, - "id": "89633751", + "execution_count": 21, + "id": "78bdc9d2", "metadata": {}, "outputs": [], "source": [ "class RandomAgent(Agent):\n", - " \"\"\"A simple agent that selects actions randomly.\"\"\"\n", + " \"\"\"A simple agent that selects actions uniformly at random (baseline).\"\"\"\n", "\n", - " def get_action( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]\n", - " self,\n", - " observation: np.ndarray,\n", - " epsilon: float = 0.0,\n", - " ) -> int:\n", - " \"\"\"Select an action randomly, ignoring the observation and epsilon.\"\"\"\n", - " _ = observation\n", - " _ = epsilon\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", + " \"\"\"Select a random action, ignoring the observation and epsilon.\"\"\"\n", + " _ = observation, epsilon\n", " return int(self.rng.integers(0, self.action_space))\n" ] }, { "cell_type": "markdown", - "id": "4b24ce3a", + "id": "5f679032", "metadata": {}, "source": [ - "## Torch Agent" + "## SARSA Agent — Linear Approximation (Semi-gradient)\n", + "\n", + "This agent combines:\n", + "- **Linear approximation** from Lab 7 (`SarsaAgent`): $\\hat{q}(s, a; \\mathbf{W}) = \\mathbf{W}_a^\\top \\phi(s)$\n", + "- **On-policy SARSA update** from Lab 5B (`train_sarsa`): $\\delta = r + \\gamma \\hat{q}(s', a') - \\hat{q}(s, a)$\n", + "\n", + "The semi-gradient update rule is:\n", + "$$W_a \\leftarrow W_a + \\alpha \\cdot \\delta \\cdot \\phi(s)$$\n", + "\n", + "where $\\phi(s)$ is the normalized observation vector (analogous to tile coding features in Lab 7, but in dense form)." ] }, { "cell_type": "code", - "execution_count": 4, - "id": "7b1dba76", + "execution_count": 22, + "id": "c124ed9a", "metadata": {}, "outputs": [], "source": [ - "class TorchAgent(Agent):\n", - " \"\"\"A reinforcement-learning agent using a PyTorch model to estimate action values.\"\"\"\n", + "class SarsaAgent(Agent):\n", + " \"\"\"Semi-gradient SARSA agent with linear function approximation.\n", + "\n", + " Inspired by:\n", + " - Lab 7 SarsaAgent: linear q(s,a) = W_a . phi(s), semi-gradient update\n", + " - Lab 5B train_sarsa: on-policy TD target using Q(s', a')\n", + "\n", + " The weight matrix W has shape (n_actions, n_features).\n", + " For a given state s, q(s, a) = W[a] @ phi(s) is the dot product\n", + " of the action's weight row with the normalized observation.\n", + " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " n_features: int,\n", " n_actions: int,\n", - " lr: float = 0.001,\n", + " alpha: float = 0.001,\n", " gamma: float = 0.99,\n", " seed: int = 42,\n", " ) -> None:\n", - " \"\"\"Initialize TorchAgent.\n", + " \"\"\"Initialize SARSA agent with linear weights.\n", "\n", " Args:\n", - " n_features: Number of input features (state dimensionality).\n", - " n_actions: Number of possible discrete actions.\n", - " lr: Learning rate (kept for subclasses).\n", + " n_features: Dimension of the feature vector phi(s).\n", + " n_actions: Number of discrete actions.\n", + " alpha: Learning rate (kept small for high-dim features).\n", " gamma: Discount factor.\n", " seed: RNG seed for reproducibility.\n", "\n", " \"\"\"\n", - " # Initialize base Agent with seed and action space size\n", " super().__init__(seed, n_actions)\n", - "\n", - " # keep parameters to avoid unused-argument warnings and for subclasses\n", " self.n_features = n_features\n", - " self.lr = lr\n", - "\n", + " self.alpha = alpha\n", " self.gamma = gamma\n", - " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", - " self.model: nn.Module | None = None\n", - " self.optimizer: optim.Optimizer | None = None\n", - " self.criterion = nn.MSELoss()\n", + " # Weight matrix: one row per action, analogous to Lab 7's self.w\n", + " # but organized as (n_actions, n_features) for dense features.\n", + " self.W = np.zeros((n_actions, n_features), dtype=np.float64)\n", "\n", - " def save(self, filename: str) -> None:\n", - " \"\"\"Save model parameters to disk.\"\"\"\n", - " if self.model is None:\n", - " message = \"Model is not initialized. Define self.model before calling save().\"\n", - " raise ValueError(message)\n", - " torch.save(self.model.state_dict(), filename)\n", + " def _q_values(self, phi: np.ndarray) -> np.ndarray:\n", + " \"\"\"Compute Q-values for all actions given feature vector phi(s).\n", "\n", - " def load(self, filename: str) -> None:\n", - " \"\"\"Load model parameters from disk into existing model.\"\"\"\n", - " if self.model is None:\n", - " message = \"Model is not initialized. Define self.model before calling load().\"\n", - " raise ValueError(message)\n", - " state = torch.load(filename, map_location=self.device)\n", - " self.model.load_state_dict(state)\n", - " self.model.eval()\n", + " Equivalent to Lab 7's self.q(s, a) = self.w[idx].sum()\n", + " but using dense linear approximation: q(s, a) = W[a] @ phi.\n", "\n", - " def get_action( # type: ignore[override] # pyright: ignore[reportIncompatibleMethodOverride]\n", - " self,\n", - " observation: np.ndarray,\n", - " epsilon: float = 0.0,\n", - " ) -> int:\n", - " \"\"\"Return an action for the given observation using epsilon-greedy policy.\"\"\"\n", - " if self.model is None:\n", - " message = \"Model is not initialized. Define self.model before calling get_action().\"\n", - " raise ValueError(message)\n", + " Args:\n", + " phi: Normalized feature vector, shape (n_features,).\n", "\n", - " if self.rng.random() < epsilon:\n", - " return int(self.rng.integers(0, self.action_space))\n", + " Returns:\n", + " Array of Q-values, shape (n_actions,).\n", "\n", - " state_t = (\n", - " torch.as_tensor(observation, dtype=torch.float32, device=self.device)\n", - " / 255.0\n", - " )\n", - " if state_t.ndim == 1:\n", - " state_t = state_t.unsqueeze(0)\n", + " \"\"\"\n", + " return self.W @ phi # shape (n_actions,)\n", "\n", - " self.model.eval()\n", - " with torch.no_grad():\n", - " q_values = self.model(state_t).squeeze(0)\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", + " \"\"\"Select action using ε-greedy policy over linear Q-values.\n", "\n", - " return int(torch.argmax(q_values).item())\n" - ] - }, - { - "cell_type": "markdown", - "id": "07150351", - "metadata": {}, - "source": [ - "## Linear Agent" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "19ed1ba8", - "metadata": {}, - "outputs": [], - "source": [ - "class LinearAgent(TorchAgent):\n", - " \"\"\"An agent that uses a simple linear model to estimate action values.\"\"\"\n", - "\n", - " def __init__(\n", - " self,\n", - " n_features: int,\n", - " n_actions: int,\n", - " lr: float = 0.001,\n", - " gamma: float = 0.99,\n", - " method: str = \"q_learning\",\n", - " ) -> None:\n", - " \"\"\"Initialize LinearAgent with a linear model and specified update method.\"\"\"\n", - " super().__init__(n_features, n_actions, lr, gamma)\n", - " self.method = method\n", - " self.model = nn.Sequential(\n", - " nn.Flatten(),\n", - " nn.Linear(n_features, n_actions),\n", - " ).to(self.device)\n", - " self.optimizer = optim.Adam(self.model.parameters(), lr=lr)\n", + " Same pattern as Lab 7 SarsaAgent.eps_greedy:\n", + " compute q-values for all actions, then apply epsilon_greedy.\n", + " \"\"\"\n", + " phi = normalize_obs(observation)\n", + " q_vals = self._q_values(phi)\n", + " return epsilon_greedy(q_vals, epsilon, self.rng)\n", "\n", " def update(\n", " self,\n", @@ -239,66 +317,243 @@ " done: bool,\n", " next_action: int | None = None,\n", " ) -> None:\n", - " \"\"\"Update the agent's value estimates using either Q-learning or SARSA.\"\"\"\n", - " if self.model is None:\n", - " message = \"Model is not initialized.\"\n", - " raise ValueError(message)\n", - " if self.optimizer is None:\n", - " message = \"Optimizer is not initialized.\"\n", - " raise ValueError(message)\n", + " \"\"\"Perform one semi-gradient SARSA update.\n", "\n", - " model = self.model\n", - " optimizer = self.optimizer\n", + " Follows the SARSA update from Lab 5B train_sarsa:\n", + " td_target = r + gamma * Q(s', a') * (0 if done else 1)\n", + " Q(s, a) += alpha * (td_target - Q(s, a))\n", "\n", - " state_t = torch.as_tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0) / 255.0\n", - " next_state_t = torch.as_tensor(next_state, device=self.device, dtype=torch.float32).unsqueeze(0) / 255.0\n", - " reward_t = torch.as_tensor(reward, device=self.device, dtype=torch.float32)\n", - " done_t = torch.as_tensor(float(done), device=self.device, dtype=torch.float32)\n", + " In continuous form with linear approximation (Lab 7 SarsaAgent.update):\n", + " delta = target - q(s, a)\n", + " W[a] += alpha * delta * phi(s)\n", "\n", - " q_curr = model(state_t).squeeze(0)[action]\n", + " Args:\n", + " state: Current observation.\n", + " action: Action taken.\n", + " reward: Reward received.\n", + " next_state: Next observation.\n", + " done: Whether the episode ended.\n", + " next_action: Action chosen in next state (required for SARSA).\n", "\n", - " with torch.no_grad():\n", - " if done:\n", - " q_next = torch.tensor(0.0, device=self.device)\n", - " elif self.method == \"q_learning\":\n", - " q_next = model(next_state_t).squeeze(0).max()\n", - " elif self.method == \"sarsa\":\n", - " if next_action is None:\n", - " message = \"next_action is required for SARSA updates.\"\n", - " raise ValueError(message)\n", - " q_next = model(next_state_t).squeeze(0)[next_action]\n", - " else:\n", - " message = (\n", - " f\"Unknown method: {self.method}. Supported methods are \"\n", - " \"'q_learning' and 'sarsa'.\"\n", - " )\n", - " raise ValueError(message)\n", + " \"\"\"\n", + " phi = np.nan_to_num(normalize_obs(state), nan=0.0, posinf=0.0, neginf=0.0)\n", + " q_sa = float(self.W[action] @ phi) # current estimate q(s, a)\n", + " if not np.isfinite(q_sa):\n", + " q_sa = 0.0\n", "\n", - " target = reward_t + (1.0 - done_t) * self.gamma * q_next\n", + " if done:\n", + " # Terminal: no future value (Lab 5B: gamma * Q[s2, a2] * 0)\n", + " target = reward\n", + " else:\n", + " # On-policy: use q(s', a') where a' is the actual next action\n", + " # This is the key SARSA property (Lab 5B)\n", + " phi_next = np.nan_to_num(normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0)\n", + " if next_action is None:\n", + " next_action = 0 # fallback, should not happen in practice\n", + " q_sp_ap = float(self.W[next_action] @ phi_next)\n", + " if not np.isfinite(q_sp_ap):\n", + " q_sp_ap = 0.0\n", + " target = float(reward) + self.gamma * q_sp_ap\n", "\n", - " loss = self.criterion(q_curr, target)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n" + " # Semi-gradient update: W[a] += alpha * delta * phi(s)\n", + " # Analogous to Lab 7: self.w[idx] += self.alpha * delta\n", + " if not np.isfinite(target):\n", + " return\n", + "\n", + " delta = float(target - q_sa)\n", + " if not np.isfinite(delta):\n", + " return\n", + "\n", + " td_step = float(np.clip(delta, -1_000.0, 1_000.0))\n", + " self.W[action] += self.alpha * td_step * phi\n", + " self.W[action] = np.nan_to_num(self.W[action], nan=0.0, posinf=1e6, neginf=-1e6)\n" ] }, { "cell_type": "markdown", - "id": "1b132c0e", + "id": "d4e18536", "metadata": {}, "source": [ - "## DQN Agent" + "## Q-Learning Agent — Linear Approximation (Off-policy)\n", + "\n", + "Same architecture as SARSA but with the **off-policy update** from Lab 5B (`train_q_learning`):\n", + "\n", + "$$\\delta = r + \\gamma \\max_{a'} \\hat{q}(s', a') - \\hat{q}(s, a)$$\n", + "\n", + "The key difference from SARSA: we use $\\max_{a'} Q(s', a')$ instead of $Q(s', a')$ where $a'$ is the action actually chosen. This allows learning the optimal policy independently of the exploration policy." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "d90b82d0", + "execution_count": 23, + "id": "f5b5b9ea", "metadata": {}, "outputs": [], "source": [ - "class DQNAgent(TorchAgent):\n", - " \"\"\"An agent that uses a deep neural network to estimate action values, with support for experience replay and target networks.\"\"\"\n", + "class QLearningAgent(Agent):\n", + " \"\"\"Q-Learning agent with linear function approximation (off-policy).\n", + "\n", + " Inspired by:\n", + " - Lab 5B train_q_learning: off-policy TD target using max_a' Q(s', a')\n", + " - Lab 7 SarsaAgent: linear approximation q(s,a) = W[a] @ phi(s)\n", + "\n", + " The only difference from SarsaAgent is the TD target:\n", + " SARSA uses Q(s', a') (on-policy), Q-Learning uses max_a' Q(s', a') (off-policy).\n", + " \"\"\"\n", + "\n", + " def __init__(\n", + " self,\n", + " n_features: int,\n", + " n_actions: int,\n", + " alpha: float = 0.001,\n", + " gamma: float = 0.99,\n", + " seed: int = 42,\n", + " ) -> None:\n", + " \"\"\"Initialize Q-Learning agent with linear weights.\n", + "\n", + " Args:\n", + " n_features: Dimension of the feature vector phi(s).\n", + " n_actions: Number of discrete actions.\n", + " alpha: Learning rate.\n", + " gamma: Discount factor.\n", + " seed: RNG seed.\n", + "\n", + " \"\"\"\n", + " super().__init__(seed, n_actions)\n", + " self.n_features = n_features\n", + " self.alpha = alpha\n", + " self.gamma = gamma\n", + " self.W = np.zeros((n_actions, n_features), dtype=np.float64)\n", + "\n", + " def _q_values(self, phi: np.ndarray) -> np.ndarray:\n", + " \"\"\"Compute Q-values for all actions: q(s, a) = W[a] @ phi for each a.\"\"\"\n", + " return self.W @ phi\n", + "\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", + " \"\"\"Select action using ε-greedy policy over linear Q-values.\"\"\"\n", + " phi = normalize_obs(observation)\n", + " q_vals = self._q_values(phi)\n", + " return epsilon_greedy(q_vals, epsilon, self.rng)\n", + "\n", + " def update(\n", + " self,\n", + " state: np.ndarray,\n", + " action: int,\n", + " reward: float,\n", + " next_state: np.ndarray,\n", + " done: bool,\n", + " next_action: int | None = None,\n", + " ) -> None:\n", + " \"\"\"Perform one Q-learning update.\n", + "\n", + " Follows Lab 5B train_q_learning:\n", + " td_target = r + gamma * max(Q[s2]) * (0 if terminated else 1)\n", + " Q[s, a] += alpha * (td_target - Q[s, a])\n", + "\n", + " In continuous form with linear approximation:\n", + " delta = target - q(s, a)\n", + " W[a] += alpha * delta * phi(s)\n", + " \"\"\"\n", + " _ = next_action # Q-learning is off-policy: next_action is not used\n", + " phi = np.nan_to_num(normalize_obs(state), nan=0.0, posinf=0.0, neginf=0.0)\n", + " q_sa = float(self.W[action] @ phi)\n", + " if not np.isfinite(q_sa):\n", + " q_sa = 0.0\n", + "\n", + " if done:\n", + " # Terminal state: no future value\n", + " # Lab 5B: gamma * np.max(Q[s2]) * (0 if terminated else 1)\n", + " target = reward\n", + " else:\n", + " # Off-policy: use max over all actions in next state\n", + " # This is the key Q-learning property (Lab 5B)\n", + " phi_next = np.nan_to_num(normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0)\n", + " q_next_all = self._q_values(phi_next) # q(s', a') for all a'\n", + " q_next_max = float(np.max(q_next_all))\n", + " if not np.isfinite(q_next_max):\n", + " q_next_max = 0.0\n", + " target = float(reward) + self.gamma * q_next_max\n", + "\n", + " if not np.isfinite(target):\n", + " return\n", + "\n", + " delta = float(target - q_sa)\n", + " if not np.isfinite(delta):\n", + " return\n", + "\n", + " td_step = float(np.clip(delta, -1_000.0, 1_000.0))\n", + " self.W[action] += self.alpha * td_step * phi\n", + " self.W[action] = np.nan_to_num(self.W[action], nan=0.0, posinf=1e6, neginf=-1e6)\n" + ] + }, + { + "cell_type": "markdown", + "id": "f644e2ef", + "metadata": {}, + "source": [ + "## DQN Agent — NumPy MLP with Experience Replay and Target Network\n", + "\n", + "This agent implements the Deep Q-Network (DQN) **entirely in numpy**, without PyTorch.\n", + "\n", + "**Network architecture** (identical to the original DQNAgent structure):\n", + "$$\\text{Input}(n\\_features) \\to \\text{Linear}(128) \\to \\text{ReLU} \\to \\text{Linear}(128) \\to \\text{ReLU} \\to \\text{Linear}(n\\_actions)$$\n", + "\n", + "**Key techniques** (inspired by Lab 6A Dyna-Q + classic DQN):\n", + "- **Experience Replay**: like the `self.model` dict in Dyna-Q (Lab 6A) that stores past transitions, we store transitions in a circular buffer and sample minibatches for updates.\n", + "- **Target Network**: periodically synchronized copy of the network, stabilizes learning.\n", + "- **Manual backpropagation**: MSE loss gradient backpropagated layer by layer." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ec090a9a", + "metadata": {}, + "outputs": [], + "source": [ + "def _relu(x: np.ndarray) -> np.ndarray:\n", + " \"\"\"ReLU activation: max(0, x).\"\"\"\n", + " return np.maximum(0, x)\n", + "\n", + "\n", + "def _relu_grad(x: np.ndarray) -> np.ndarray:\n", + " \"\"\"Compute the ReLU derivative: return 1 where x > 0, else 0.\"\"\"\n", + " return (x > 0).astype(np.float64)\n", + "\n", + "\n", + "def _init_layer(\n", + " fan_in: int, fan_out: int, rng: np.random.Generator,\n", + ") -> tuple[np.ndarray, np.ndarray]:\n", + " \"\"\"Initialize a linear layer with He initialization.\n", + "\n", + " He init: W ~ N(0, sqrt(2/fan_in)) — standard for ReLU networks.\n", + "\n", + " Args:\n", + " fan_in: Number of input features.\n", + " fan_out: Number of output features.\n", + " rng: Random number generator.\n", + "\n", + " Returns:\n", + " Weight matrix W of shape (fan_in, fan_out) and bias vector b of shape (fan_out,).\n", + "\n", + " \"\"\"\n", + " scale = np.sqrt(2.0 / fan_in)\n", + " W = rng.normal(0, scale, size=(fan_in, fan_out)).astype(np.float64)\n", + " b = np.zeros(fan_out, dtype=np.float64)\n", + " return W, b\n", + "\n", + "\n", + "class DQNAgent(Agent):\n", + " \"\"\"Deep Q-Network agent with a 2-hidden-layer MLP implemented in pure numpy.\n", + "\n", + " Architecture: Input -> Linear(128) -> ReLU -> Linear(128) -> ReLU -> Linear(n_actions)\n", + " Matches the original PyTorch DQNAgent structure.\n", + "\n", + " Features:\n", + " - Experience replay buffer (like Dyna-Q model in Lab 6A: store past transitions, sample for updates)\n", + " - Target network (periodically synchronized copy for stable targets)\n", + " - Manual forward pass and backpropagation through the MLP\n", + " \"\"\"\n", "\n", " def __init__(\n", " self,\n", @@ -309,36 +564,131 @@ " buffer_size: int = 10000,\n", " batch_size: int = 64,\n", " target_update_freq: int = 1000,\n", + " seed: int = 42,\n", " ) -> None:\n", - " \"\"\"Initialize DQNAgent with a deep neural network, experience replay buffer, and target network.\"\"\"\n", - " super().__init__(n_features, n_actions, lr, gamma)\n", + " \"\"\"Initialize DQN agent.\n", + "\n", + " Args:\n", + " n_features: Input feature dimension.\n", + " n_actions: Number of discrete actions.\n", + " lr: Learning rate for gradient descent.\n", + " gamma: Discount factor.\n", + " buffer_size: Maximum size of the replay buffer.\n", + " batch_size: Minibatch size for updates.\n", + " target_update_freq: Steps between target network syncs.\n", + " seed: RNG seed.\n", + "\n", + " \"\"\"\n", + " super().__init__(seed, n_actions)\n", + " self.n_features = n_features\n", + " self.lr = lr\n", + " self.gamma = gamma\n", " self.buffer_size = buffer_size\n", " self.batch_size = batch_size\n", " self.target_update_freq = target_update_freq\n", " self.update_step = 0\n", "\n", - " self.model = nn.Sequential(\n", - " nn.Flatten(),\n", - " nn.Linear(n_features, 128),\n", - " nn.ReLU(),\n", - " nn.Linear(128, 128),\n", - " nn.ReLU(),\n", - " nn.Linear(128, n_actions),\n", - " ).to(self.device)\n", + " # Initialize network parameters: 3 layers\n", + " # Layer 1: n_features -> 128\n", + " # Layer 2: 128 -> 128\n", + " # Layer 3: 128 -> n_actions\n", + " self.params = self._init_params(self.rng)\n", "\n", - " self.target_model: nn.Module = nn.Sequential(\n", - " nn.Flatten(),\n", - " nn.Linear(n_features, 128),\n", - " nn.ReLU(),\n", - " nn.Linear(128, 128),\n", - " nn.ReLU(),\n", - " nn.Linear(128, n_actions),\n", - " ).to(self.device)\n", - " self.target_model.load_state_dict(self.model.state_dict())\n", + " # Target network: deep copy of params (like Dyna-Q's model copy concept)\n", + " self.target_params = {k: v.copy() for k, v in self.params.items()}\n", "\n", - " self.optimizer = optim.Adam(self.model.parameters(), lr=lr)\n", + " # Experience replay buffer: list of (s, a, r, s', done) tuples\n", + " # Analogous to Dyna-Q's self.model dict + self.observed_sa in Lab 6A,\n", + " # but storing raw transitions for off-policy sampling\n", " self.replay_buffer: list[tuple[np.ndarray, int, float, np.ndarray, bool]] = []\n", "\n", + " def _init_params(self, rng: np.random.Generator) -> dict[str, np.ndarray]:\n", + " \"\"\"Initialize all MLP parameters with He initialization.\"\"\"\n", + " W1, b1 = _init_layer(self.n_features, 128, rng)\n", + " W2, b2 = _init_layer(128, 128, rng)\n", + " W3, b3 = _init_layer(128, self.action_space, rng)\n", + " return {\"W1\": W1, \"b1\": b1, \"W2\": W2, \"b2\": b2, \"W3\": W3, \"b3\": b3}\n", + "\n", + " def _forward(\n", + " self, x: np.ndarray, params: dict[str, np.ndarray],\n", + " ) -> tuple[np.ndarray, dict[str, np.ndarray]]:\n", + " \"\"\"Forward pass through the MLP. Returns output and cached activations for backprop.\n", + "\n", + " Architecture: x -> Linear -> ReLU -> Linear -> ReLU -> Linear -> output\n", + "\n", + " Args:\n", + " x: Input array of shape (batch, n_features) or (n_features,).\n", + " params: Dictionary of network parameters.\n", + "\n", + " Returns:\n", + " output: Q-values of shape (batch, n_actions) or (n_actions,).\n", + " cache: Dictionary of intermediate values for backpropagation.\n", + "\n", + " \"\"\"\n", + " # Layer 1\n", + " z1 = x @ params[\"W1\"] + params[\"b1\"]\n", + " h1 = _relu(z1)\n", + " # Layer 2\n", + " z2 = h1 @ params[\"W2\"] + params[\"b2\"]\n", + " h2 = _relu(z2)\n", + " # Layer 3 (output, no activation)\n", + " out = h2 @ params[\"W3\"] + params[\"b3\"]\n", + " cache = {\"x\": x, \"z1\": z1, \"h1\": h1, \"z2\": z2, \"h2\": h2}\n", + " return out, cache\n", + "\n", + " def _backward(\n", + " self,\n", + " d_out: np.ndarray,\n", + " cache: dict[str, np.ndarray],\n", + " params: dict[str, np.ndarray],\n", + " ) -> dict[str, np.ndarray]:\n", + " \"\"\"Backward pass: compute gradients of loss w.r.t. all parameters.\n", + "\n", + " Args:\n", + " d_out: Gradient of loss w.r.t. output, shape (batch, n_actions).\n", + " cache: Cached activations from forward pass.\n", + " params: Current network parameters.\n", + "\n", + " Returns:\n", + " Dictionary of gradients for each parameter.\n", + "\n", + " \"\"\"\n", + " batch = d_out.shape[0] if d_out.ndim > 1 else 1\n", + " if d_out.ndim == 1:\n", + " d_out = d_out.reshape(1, -1)\n", + "\n", + " x = cache[\"x\"] if cache[\"x\"].ndim > 1 else cache[\"x\"].reshape(1, -1)\n", + " h1 = cache[\"h1\"] if cache[\"h1\"].ndim > 1 else cache[\"h1\"].reshape(1, -1)\n", + " h2 = cache[\"h2\"] if cache[\"h2\"].ndim > 1 else cache[\"h2\"].reshape(1, -1)\n", + " z1 = cache[\"z1\"] if cache[\"z1\"].ndim > 1 else cache[\"z1\"].reshape(1, -1)\n", + " z2 = cache[\"z2\"] if cache[\"z2\"].ndim > 1 else cache[\"z2\"].reshape(1, -1)\n", + "\n", + " # Layer 3 gradients\n", + " dW3 = h2.T @ d_out / batch\n", + " db3 = d_out.mean(axis=0)\n", + " dh2 = d_out @ params[\"W3\"].T\n", + "\n", + " # Layer 2 gradients (through ReLU)\n", + " dz2 = dh2 * _relu_grad(z2)\n", + " dW2 = h1.T @ dz2 / batch\n", + " db2 = dz2.mean(axis=0)\n", + " dh1 = dz2 @ params[\"W2\"].T\n", + "\n", + " # Layer 1 gradients (through ReLU)\n", + " dz1 = dh1 * _relu_grad(z1)\n", + " dW1 = x.T @ dz1 / batch\n", + " db1 = dz1.mean(axis=0)\n", + "\n", + " return {\"W1\": dW1, \"b1\": db1, \"W2\": dW2, \"b2\": db2, \"W3\": dW3, \"b3\": db3}\n", + "\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", + " \"\"\"Select action using ε-greedy policy over MLP Q-values.\"\"\"\n", + " phi = normalize_obs(observation)\n", + " q_vals, _ = self._forward(phi, self.params)\n", + " if q_vals.ndim > 1:\n", + " q_vals = q_vals.squeeze(0)\n", + " return epsilon_greedy(q_vals, epsilon, self.rng)\n", + "\n", " def update(\n", " self,\n", " state: np.ndarray,\n", @@ -348,90 +698,144 @@ " done: bool,\n", " next_action: int | None = None,\n", " ) -> None:\n", - " \"\"\"Update the agent's value estimates using experience replay and target network.\"\"\"\n", - " _ = next_action # kept for API compatibility with other agents\n", + " \"\"\"Store transition and perform a minibatch DQN update.\n", "\n", - " if self.model is None:\n", - " message = \"Model is not initialized.\"\n", - " raise ValueError(message)\n", - " if self.optimizer is None:\n", - " message = \"Optimizer is not initialized.\"\n", - " raise ValueError(message)\n", + " Steps:\n", + " 1. Add transition to replay buffer (like Dyna-Q model update in Lab 6A)\n", + " 2. If buffer has enough samples, sample a minibatch\n", + " 3. Compute targets using target network (Q-learning: max_a' Q_target(s', a'))\n", + " 4. Backpropagate MSE loss through the MLP\n", + " 5. Update weights with gradient descent\n", + " 6. Periodically sync target network\n", "\n", - " model = self.model\n", - " optimizer = self.optimizer\n", - " target_model = self.target_model\n", + " \"\"\"\n", + " _ = next_action # DQN is off-policy\n", "\n", - " self.replay_buffer.append((state, action, reward, next_state, done))\n", + " # Store transition in replay buffer\n", + " # Analogous to Lab 6A: self.model[(s,a)] = (r, sp)\n", + " phi_s = normalize_obs(state)\n", + " phi_sp = normalize_obs(next_state)\n", + " self.replay_buffer.append((phi_s, action, reward, phi_sp, done))\n", " if len(self.replay_buffer) > self.buffer_size:\n", " self.replay_buffer.pop(0)\n", "\n", + " # Don't update until we have enough samples\n", " if len(self.replay_buffer) < self.batch_size:\n", " return\n", "\n", - " idx = self.rng.choice(\n", - " len(self.replay_buffer), size=self.batch_size, replace=False,\n", - " )\n", + " # Sample minibatch from replay buffer\n", + " # Like Dyna-Q planning: sample from observed transitions (Lab 6A)\n", + " idx = self.rng.choice(len(self.replay_buffer), size=self.batch_size, replace=False)\n", " batch = [self.replay_buffer[i] for i in idx]\n", "\n", - " states_np, actions_np, rewards_np, next_states_np, dones_np = map(\n", - " np.array, zip(*batch, strict=True),\n", - " )\n", + " states_b = np.array([t[0] for t in batch]) # (batch, n_features)\n", + " actions_b = np.array([t[1] for t in batch]) # (batch,)\n", + " rewards_b = np.array([t[2] for t in batch]) # (batch,)\n", + " next_states_b = np.array([t[3] for t in batch]) # (batch, n_features)\n", + " dones_b = np.array([t[4] for t in batch], dtype=np.float64)\n", "\n", - " states_t = torch.as_tensor(states_np, dtype=torch.float32, device=self.device) / 255.0\n", - " next_states_t = torch.as_tensor(next_states_np, dtype=torch.float32, device=self.device) / 255.0\n", - " actions_t = torch.as_tensor(actions_np, dtype=torch.long, device=self.device)\n", - " rewards_t = torch.as_tensor(rewards_np, dtype=torch.float32, device=self.device)\n", - " dones_t = torch.as_tensor(\n", - " dones_np.astype(float), dtype=torch.float32, device=self.device,\n", - " )\n", + " # Forward pass on current states\n", + " q_all, cache = self._forward(states_b, self.params)\n", + " # Gather Q-values for the taken actions\n", + " q_curr = q_all[np.arange(self.batch_size), actions_b] # (batch,)\n", "\n", - " q_curr = model(states_t).gather(1, actions_t.unsqueeze(1)).squeeze(1)\n", + " # Compute targets using target network (off-policy: max over actions)\n", + " # Follows Lab 5B Q-learning: td_target = r + gamma * max(Q[s2]) * (0 if terminated else 1)\n", + " q_next_all, _ = self._forward(next_states_b, self.target_params)\n", + " q_next_max = np.max(q_next_all, axis=1) # (batch,)\n", + " targets = rewards_b + (1.0 - dones_b) * self.gamma * q_next_max\n", "\n", - " with torch.no_grad():\n", - " q_next = target_model(next_states_t).max(dim=1)[0]\n", - " target = rewards_t + (1.0 - dones_t) * self.gamma * q_next\n", + " # MSE loss gradient: d_loss/d_q_curr = 2 * (q_curr - targets) / batch\n", + " # We only backprop through the action that was taken\n", + " d_out = np.zeros_like(q_all)\n", + " d_out[np.arange(self.batch_size), actions_b] = 2.0 * (q_curr - targets) / self.batch_size\n", "\n", - " loss = self.criterion(q_curr, target)\n", + " # Backward pass\n", + " grads = self._backward(d_out, cache, self.params)\n", "\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", + " # Gradient descent update\n", + " for key in self.params:\n", + " self.params[key] -= self.lr * grads[key]\n", "\n", + " # Sync target network periodically\n", " self.update_step += 1\n", " if self.update_step % self.target_update_freq == 0:\n", - " target_model.load_state_dict(model.state_dict())\n" + " self.target_params = {k: v.copy() for k, v in self.params.items()}\n" ] }, { "cell_type": "markdown", - "id": "2014cc3e", + "id": "b7b63455", "metadata": {}, "source": [ - "## Monte Carlo Agent" + "## Monte Carlo Agent — Linear Approximation (First-visit)\n", + "\n", + "This agent is inspired by Lab 4 (`mc_control_epsilon_soft`):\n", + "- Accumulates transitions in an episode buffer `(state, action, reward)`\n", + "- At the end of the episode (`done=True`), computes **cumulative returns** by traversing the buffer backward:\n", + " $$G \\leftarrow \\gamma \\cdot G + r$$\n", + "- Updates weights with the semi-gradient rule:\n", + " $$W_a \\leftarrow W_a + \\alpha \\cdot (G - \\hat{q}(s, a)) \\cdot \\phi(s)$$\n", + "\n", + "Unlike TD methods (SARSA, Q-Learning), Monte Carlo waits for the complete episode to finish before updating." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "f99050ce", + "execution_count": 25, + "id": "3c9d74be", "metadata": {}, "outputs": [], "source": [ - "class MonteCarloAgent(LinearAgent):\n", - " \"\"\"An agent that uses Monte Carlo methods to estimate action values.\"\"\"\n", + "class MonteCarloAgent(Agent):\n", + " \"\"\"Monte Carlo control agent with linear function approximation.\n", + "\n", + " Inspired by Lab 4 mc_control_epsilon_soft:\n", + " - Accumulates transitions in an episode buffer\n", + " - At episode end (done=True), computes discounted returns backward:\n", + " G = gamma * G + r (same as Lab 4's reversed loop)\n", + " - Updates weights with semi-gradient: W[a] += alpha * (G - q(s,a)) * phi(s)\n", + "\n", + " Unlike TD methods (SARSA, Q-Learning), no update occurs until the episode ends.\n", + " \"\"\"\n", "\n", " def __init__(\n", " self,\n", " n_features: int,\n", " n_actions: int,\n", - " lr: float = 0.001,\n", + " alpha: float = 0.001,\n", " gamma: float = 0.99,\n", + " seed: int = 42,\n", " ) -> None:\n", - " \"\"\"Initialize MonteCarloAgent.\"\"\"\n", - " super().__init__(n_features, n_actions, lr, gamma, method=\"monte_carlo\")\n", + " \"\"\"Initialize Monte Carlo agent.\n", + "\n", + " Args:\n", + " n_features: Dimension of the feature vector phi(s).\n", + " n_actions: Number of discrete actions.\n", + " alpha: Learning rate.\n", + " gamma: Discount factor.\n", + " seed: RNG seed.\n", + "\n", + " \"\"\"\n", + " super().__init__(seed, n_actions)\n", + " self.n_features = n_features\n", + " self.alpha = alpha\n", + " self.gamma = gamma\n", + " self.W = np.zeros((n_actions, n_features), dtype=np.float64)\n", + " # Episode buffer: stores (state, action, reward) tuples\n", + " # Analogous to Lab 4's episode list in generate_episode\n", " self.episode_buffer: list[tuple[np.ndarray, int, float]] = []\n", "\n", + " def _q_values(self, phi: np.ndarray) -> np.ndarray:\n", + " \"\"\"Compute Q-values for all actions: q(s, a) = W[a] @ phi for each a.\"\"\"\n", + " return self.W @ phi\n", + "\n", + " def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n", + " \"\"\"Select action using ε-greedy policy over linear Q-values.\"\"\"\n", + " phi = normalize_obs(observation)\n", + " q_vals = self._q_values(phi)\n", + " return epsilon_greedy(q_vals, epsilon, self.rng)\n", + "\n", " def update(\n", " self,\n", " state: np.ndarray,\n", @@ -441,344 +845,593 @@ " done: bool,\n", " next_action: int | None = None,\n", " ) -> None:\n", - " \"\"\"Update value estimates with Monte Carlo returns at episode end.\"\"\"\n", - " _ = next_state\n", - " _ = next_action\n", + " \"\"\"Accumulate transitions and update at episode end with MC returns.\n", "\n", - " if self.model is None:\n", - " msg = \"Model is not initialized.\"\n", - " raise ValueError(msg)\n", - " if self.optimizer is None:\n", - " msg = \"Optimizer is not initialized.\"\n", - " raise ValueError(msg)\n", + " Follows Lab 4 mc_control_epsilon_soft / mc_control_exploring_starts:\n", + " 1. Append (state, action, reward) to episode buffer\n", + " 2. If not done: wait (no update yet)\n", + " 3. If done: compute returns backward and update weights\n", "\n", - " model = self.model\n", - " optimizer = self.optimizer\n", + " The backward loop is exactly the Lab 4 pattern:\n", + " G = 0\n", + " for s, a, r in reversed(episode_buffer):\n", + " G = gamma * G + r\n", + " # update Q(s, a) toward G\n", + " \"\"\"\n", + " _ = next_state, next_action # Not used in MC\n", "\n", " self.episode_buffer.append((state, action, reward))\n", "\n", " if not done:\n", - " return\n", + " return # Wait until episode ends\n", "\n", + " # Episode finished: compute MC returns and update\n", + " # Backward pass through episode (Lab 4 pattern)\n", " returns = 0.0\n", " for s, a, r in reversed(self.episode_buffer):\n", " returns = self.gamma * returns + r\n", "\n", - " state_t = torch.as_tensor(s, dtype=torch.float32, device=self.device)\n", - " if state_t.ndim == 1:\n", - " state_t = state_t.unsqueeze(0)\n", + " phi = np.nan_to_num(normalize_obs(s), nan=0.0, posinf=0.0, neginf=0.0)\n", + " q_sa = float(self.W[a] @ phi)\n", + " if not np.isfinite(q_sa):\n", + " q_sa = 0.0\n", "\n", - " q_val = model(state_t).squeeze(0)[a]\n", - " target = torch.as_tensor(returns, dtype=torch.float32, device=self.device)\n", + " # Semi-gradient update toward the MC return G\n", + " # Analogous to Lab 4: Q[(s,a)] += (G - Q[(s,a)]) / N[(s,a)]\n", + " # but with linear approximation and fixed step size\n", + " if not np.isfinite(returns):\n", + " continue\n", "\n", - " loss = self.criterion(q_val, target)\n", - " optimizer.zero_grad()\n", - " loss.backward()\n", - " optimizer.step()\n", + " delta = float(returns - q_sa)\n", + " if not np.isfinite(delta):\n", + " continue\n", "\n", + " td_step = float(np.clip(delta, -1_000.0, 1_000.0))\n", + " self.W[a] += self.alpha * td_step * phi\n", + " self.W[a] = np.nan_to_num(self.W[a], nan=0.0, posinf=1e6, neginf=-1e6)\n", + "\n", + " # Clear episode buffer for next episode\n", " self.episode_buffer = []\n" ] }, { "cell_type": "markdown", - "id": "bfa23713", + "id": "91e51dc8", "metadata": {}, "source": [ - "# Tennis Environment" + "## Tennis Environment\n", + "\n", + "Creation of the Atari Tennis environment via Gymnasium (`ALE/Tennis-v5`) with standard wrappers:\n", + "- **Grayscale**: `obs_type=\"grayscale\"` — single-channel observations\n", + "- **Resize**: `ResizeObservation(84, 84)` — downscale to 84×84\n", + "- **Frame stack**: `FrameStackObservation(4)` — stack 4 consecutive frames\n", + "\n", + "The final observation is an array of shape `(4, 84, 84)`, which flattens to 28,224 features.\n", + "\n", + "The agent plays against the **built-in Atari AI opponent**." ] }, { "cell_type": "code", - "execution_count": 8, - "id": "96c06fe3", + "execution_count": 26, + "id": "f9a973dd", "metadata": {}, "outputs": [], "source": [ - "def create_env(obs_type: str = \"ram\"):\n", - " \"\"\"Create the Tennis environment with optional preprocessing for image observations.\"\"\"\n", - " env = tennis_v3.env(obs_type=obs_type)\n", - " if obs_type == \"rgb_image\":\n", - " env = ss.color_reduction_v0(env, mode=\"full\")\n", - " env = ss.resize_v1(env, x_size=84, y_size=84)\n", - " return ss.frame_stack_v1(env, 4)\n" + "def create_env() -> gym.Env:\n", + " \"\"\"Create the ALE/Tennis-v5 environment with preprocessing wrappers.\n", + "\n", + " Applies:\n", + " - obs_type=\"grayscale\": grayscale observation (210, 160)\n", + " - ResizeObservation(84, 84): downscale to 84x84\n", + " - FrameStackObservation(4): stack 4 consecutive frames -> (4, 84, 84)\n", + "\n", + " Returns:\n", + " Gymnasium environment ready for training.\n", + "\n", + " \"\"\"\n", + " env = gym.make(\"ALE/Tennis-v5\", obs_type=\"grayscale\")\n", + " env = ResizeObservation(env, shape=(84, 84))\n", + " return FrameStackObservation(env, stack_size=4)\n" ] }, { "cell_type": "markdown", - "id": "fe326344", + "id": "18cb28d8", "metadata": {}, "source": [ - "## Tournament" + "## Training & Evaluation Infrastructure\n", + "\n", + "Functions for training and evaluating agents in the single-agent Gymnasium environment:\n", + "\n", + "1. **`train_agent`** — Pre-trains an agent against the built-in AI for a given number of episodes with ε-greedy exploration\n", + "2. **`evaluate_agent`** — Evaluates a trained agent (no exploration, ε = 0) and returns performance metrics\n", + "3. **`plot_training_curves`** — Plots the training reward history (moving average) for all agents\n", + "4. **`plot_evaluation_comparison`** — Bar chart comparing final evaluation scores across agents\n", + "5. **`evaluate_tournament`** — Evaluates all agents and produces a summary comparison" ] }, { "cell_type": "code", - "execution_count": 9, - "id": "7b1d6ebf", + "execution_count": 27, + "id": "06b91580", "metadata": {}, "outputs": [], "source": [ - "class AECEnvProtocol(Protocol):\n", - " \"\"\"Protocol for PettingZoo AEC environments used in tournament matches.\"\"\"\n", - "\n", - " possible_agents: list[str]\n", - "\n", - " def reset(self) -> None:\n", - " \"\"\"Reset the environment to its initial state.\"\"\"\n", - " ...\n", - "\n", - " def agent_iter(self) -> Iterator[str]:\n", - " \"\"\"Iterate over agent identifiers in turn order.\"\"\"\n", - " ...\n", - "\n", - " def last(self) -> tuple[np.ndarray, float, bool, bool, dict[str, object]]:\n", - " \"\"\"Return the last transition tuple for the current agent.\"\"\"\n", - " ...\n", - "\n", - " def step(self, action: int | None) -> None:\n", - " \"\"\"Apply an action for the current agent.\"\"\"\n", - " ...\n", - "\n", - "\n", - "def run_tournament_match(\n", - " env: AECEnvProtocol,\n", - " agent1: Agent,\n", - " agent2: Agent,\n", + "def train_agent(\n", + " env: gym.Env,\n", + " agent: Agent,\n", + " name: str,\n", " *,\n", - " episodes: int = 100,\n", - " train_mode: bool = True,\n", - " epsilon_start: float = 0.2,\n", + " episodes: int = 5000,\n", + " epsilon_start: float = 1.0,\n", " epsilon_end: float = 0.05,\n", - " epsilon_decay: float = 0.99,\n", - " max_steps: int = 2000,\n", - ") -> tuple[dict[str, int], dict[str, list[float]]]:\n", - " \"\"\"Run a tournament match between two agents in a PettingZoo AEC environment.\"\"\"\n", - " agents_dict: dict[str, Agent] = {\"first_0\": agent1, \"second_0\": agent2}\n", - " wins = {\"first_0\": 0, \"second_0\": 0}\n", - " rewards_history = {\"first_0\": [], \"second_0\": []}\n", + " epsilon_decay: float = 0.999,\n", + " max_steps: int = 5000,\n", + ") -> list[float]:\n", + " \"\"\"Pre-train an agent against the built-in Atari AI opponent.\n", "\n", - " current_epsilon = epsilon_start if train_mode else 0.0\n", + " Each agent learns independently by playing full episodes. This is the\n", + " self-play pre-training phase: the agent interacts with the environment's\n", + " built-in opponent and updates its parameters after each transition.\n", "\n", - " for _ in range(episodes):\n", - " env.reset()\n", + " Args:\n", + " env: Gymnasium ALE/Tennis-v5 environment.\n", + " agent: Agent instance to train.\n", + " name: Display name for the progress bar.\n", + " episodes: Number of training episodes.\n", + " epsilon_start: Initial exploration rate.\n", + " epsilon_end: Minimum exploration rate.\n", + " epsilon_decay: Multiplicative decay per episode.\n", + " max_steps: Maximum steps per episode.\n", "\n", - " previous_states: dict[str, np.ndarray | None] = dict.fromkeys(\n", - " env.possible_agents,\n", - " None,\n", - " )\n", - " previous_actions: dict[str, int | None] = dict.fromkeys(\n", - " env.possible_agents,\n", - " None,\n", - " )\n", - " episode_rewards = {\"first_0\": 0.0, \"second_0\": 0.0}\n", + " Returns:\n", + " List of total rewards per episode.\n", "\n", - " for steps, agent_id in enumerate(env.agent_iter()):\n", - " observation, reward, termination, truncation, _info = env.last()\n", - " done = termination or truncation\n", - " obs_array = np.asarray(observation)\n", + " \"\"\"\n", + " rewards_history: list[float] = []\n", + " epsilon = epsilon_start\n", "\n", - " episode_rewards[agent_id] += reward\n", + " pbar = tqdm(range(episodes), desc=f\"Training {name}\", leave=True)\n", "\n", - " action: int | None\n", - " if done or steps >= max_steps:\n", - " action = None\n", - " done = True\n", - " if reward > 0:\n", - " wins[agent_id] += 1\n", - " else:\n", - " action = agents_dict[agent_id].get_action(\n", - " obs_array,\n", - " epsilon=current_epsilon,\n", - " )\n", + " for _ep in pbar:\n", + " obs, _info = env.reset()\n", + " obs = np.asarray(obs)\n", + " total_reward = 0.0\n", "\n", - " if train_mode:\n", - " prev_state = previous_states[agent_id]\n", - " prev_action = previous_actions[agent_id]\n", - " if prev_state is not None and prev_action is not None:\n", - " agents_dict[agent_id].update(\n", - " state=prev_state,\n", - " action=prev_action,\n", - " reward=reward,\n", - " next_state=obs_array,\n", - " done=done,\n", - " next_action=action,\n", - " )\n", + " # Select first action\n", + " action = agent.get_action(obs, epsilon=epsilon)\n", "\n", - " if not done:\n", - " previous_states[agent_id] = obs_array\n", - " previous_actions[agent_id] = action\n", + " for _step in range(max_steps):\n", + " next_obs, reward, terminated, truncated, _info = env.step(action)\n", + " next_obs = np.asarray(next_obs)\n", + " done = terminated or truncated\n", + " reward = float(reward)\n", + " total_reward += reward\n", "\n", - " env.step(action)\n", + " # Select next action (needed for SARSA's on-policy update)\n", + " next_action = agent.get_action(next_obs, epsilon=epsilon) if not done else None\n", "\n", - " if steps + 1 >= max_steps:\n", + " # Update agent with the transition\n", + " agent.update(\n", + " state=obs,\n", + " action=action,\n", + " reward=reward,\n", + " next_state=next_obs,\n", + " done=done,\n", + " next_action=next_action,\n", + " )\n", + "\n", + " if done:\n", " break\n", "\n", - " rewards_history[\"first_0\"].append(episode_rewards[\"first_0\"])\n", - " rewards_history[\"second_0\"].append(episode_rewards[\"second_0\"])\n", + " obs = next_obs\n", + " action = next_action\n", "\n", - " if train_mode:\n", - " current_epsilon = max(epsilon_end, current_epsilon * epsilon_decay)\n", + " rewards_history.append(total_reward)\n", + " epsilon = max(epsilon_end, epsilon * epsilon_decay)\n", "\n", - " return wins, rewards_history\n", + " # Update progress bar\n", + " recent_window = 50\n", + " if len(rewards_history) >= recent_window:\n", + " recent_avg = np.mean(rewards_history[-recent_window:])\n", + " pbar.set_postfix(\n", + " avg50=f\"{recent_avg:.1f}\",\n", + " eps=f\"{epsilon:.3f}\",\n", + " rew=f\"{total_reward:.0f}\",\n", + " )\n", + "\n", + " return rewards_history\n", "\n", "\n", - "def plot_learning_curves(rewards_history: dict[str, list[float]], window: int = 50) -> None:\n", - " \"\"\"Plot learning curves for each agent using a moving average of rewards.\"\"\"\n", - " plt.figure(figsize=(10, 5))\n", + "def evaluate_agent(\n", + " env: gym.Env,\n", + " agent: Agent,\n", + " name: str,\n", + " *,\n", + " episodes: int = 20,\n", + " max_steps: int = 5000,\n", + ") -> dict[str, object]:\n", + " \"\"\"Evaluate a trained agent with no exploration (ε = 0).\n", "\n", - " for agent_id, rewards in rewards_history.items():\n", + " Args:\n", + " env: Gymnasium ALE/Tennis-v5 environment.\n", + " agent: Trained agent to evaluate.\n", + " name: Display name for the progress bar.\n", + " episodes: Number of evaluation episodes.\n", + " max_steps: Maximum steps per episode.\n", + "\n", + " Returns:\n", + " Dictionary with rewards list, mean, std, wins, and win rate.\n", + "\n", + " \"\"\"\n", + " rewards: list[float] = []\n", + " wins = 0\n", + "\n", + " for _ep in tqdm(range(episodes), desc=f\"Evaluating {name}\", leave=False):\n", + " obs, _info = env.reset()\n", + " total_reward = 0.0\n", + "\n", + " for _step in range(max_steps):\n", + " action = agent.get_action(np.asarray(obs), epsilon=0.0)\n", + " obs, reward, terminated, truncated, _info = env.step(action)\n", + " reward = float(reward)\n", + " total_reward += reward\n", + " if terminated or truncated:\n", + " break\n", + "\n", + " rewards.append(total_reward)\n", + " if total_reward > 0:\n", + " wins += 1\n", + "\n", + " return {\n", + " \"rewards\": rewards,\n", + " \"mean_reward\": float(np.mean(rewards)),\n", + " \"std_reward\": float(np.std(rewards)),\n", + " \"wins\": wins,\n", + " \"win_rate\": wins / episodes,\n", + " }\n", + "\n", + "\n", + "def plot_training_curves(\n", + " training_histories: dict[str, list[float]],\n", + " window: int = 100,\n", + ") -> None:\n", + " \"\"\"Plot training reward curves for all agents on a single figure.\n", + "\n", + " Uses a moving average to smooth the curves.\n", + "\n", + " Args:\n", + " training_histories: Dict mapping agent names to reward lists.\n", + " window: Moving average window size.\n", + "\n", + " \"\"\"\n", + " plt.figure(figsize=(12, 6))\n", + "\n", + " for name, rewards in training_histories.items():\n", " if len(rewards) >= window:\n", " ma = np.convolve(rewards, np.ones(window) / window, mode=\"valid\")\n", - " plt.plot(np.arange(window - 1, len(rewards)), ma, label=f\"Agent {agent_id}\")\n", + " plt.plot(np.arange(window - 1, len(rewards)), ma, label=name)\n", " else:\n", - " plt.plot(rewards, label=f\"Agent {agent_id} (Raw)\")\n", + " plt.plot(rewards, label=f\"{name} (raw)\")\n", "\n", " plt.xlabel(\"Episodes\")\n", - " plt.ylabel(f\"Moving Average Reward (Window={window})\")\n", + " plt.ylabel(f\"Average Reward (Window={window})\")\n", + " plt.title(\"Training Curves (vs built-in AI)\")\n", " plt.legend()\n", " plt.grid(visible=True)\n", + " plt.tight_layout()\n", + " plt.show()\n", + "\n", + "\n", + "def plot_evaluation_comparison(results: dict[str, dict[str, object]]) -> None:\n", + " \"\"\"Bar chart comparing evaluation performance of all agents.\n", + "\n", + " Args:\n", + " results: Dict mapping agent names to evaluation result dicts.\n", + "\n", + " \"\"\"\n", + " names = list(results.keys())\n", + " means = [results[n][\"mean_reward\"] for n in names]\n", + " stds = [results[n][\"std_reward\"] for n in names]\n", + " win_rates = [results[n][\"win_rate\"] for n in names]\n", + "\n", + " _fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n", + "\n", + " # Mean reward bar chart\n", + " colors = sns.color_palette(\"husl\", len(names))\n", + " axes[0].bar(names, means, yerr=stds, capsize=5, color=colors, edgecolor=\"black\")\n", + " axes[0].set_ylabel(\"Mean Reward\")\n", + " axes[0].set_title(\"Evaluation: Mean Reward per Agent (vs built-in AI)\")\n", + " axes[0].axhline(y=0, color=\"gray\", linestyle=\"--\", alpha=0.5)\n", + " axes[0].grid(axis=\"y\", alpha=0.3)\n", + "\n", + " # Win rate bar chart\n", + " axes[1].bar(names, win_rates, color=colors, edgecolor=\"black\")\n", + " axes[1].set_ylabel(\"Win Rate\")\n", + " axes[1].set_title(\"Evaluation: Win Rate per Agent (vs built-in AI)\")\n", + " axes[1].set_ylim(0, 1)\n", + " axes[1].axhline(y=0.5, color=\"gray\", linestyle=\"--\", alpha=0.5, label=\"50% baseline\")\n", + " axes[1].legend()\n", + " axes[1].grid(axis=\"y\", alpha=0.3)\n", + "\n", + " plt.tight_layout()\n", " plt.show()\n", "\n", "\n", - "def plot_win_rate_matrix(results_matrix: np.ndarray, agent_names: list[str]) -> None:\n", - " \"\"\"Plot a heatmap of win rates between agents.\"\"\"\n", - " plt.figure(figsize=(8, 6))\n", - " sns.heatmap(\n", - " results_matrix,\n", - " annot=True,\n", - " fmt=\".2f\",\n", - " cmap=\"Blues\",\n", - " xticklabels=agent_names,\n", - " yticklabels=agent_names,\n", - " )\n", - " plt.xlabel(\"Opponent\")\n", - " plt.ylabel(\"Agent\")\n", - " plt.title(\"Win Rate Matrix\")\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "81dc1a05", - "metadata": {}, - "outputs": [], - "source": [ "def evaluate_tournament(\n", - " env: AECEnvProtocol,\n", + " env: gym.Env,\n", " agents: dict[str, Agent],\n", - " episodes_per_match: int = 10,\n", - ") -> tuple[np.ndarray, list[str]]:\n", - " \"\"\"Evaluate a round-robin tournament between agents, returning a win rate matrix and agent names.\"\"\"\n", - " agent_names = list(agents.keys())\n", - " n_agents = len(agent_names)\n", - " win_matrix = np.zeros((n_agents, n_agents))\n", + " episodes_per_agent: int = 20,\n", + ") -> dict[str, dict[str, object]]:\n", + " \"\"\"Evaluate all agents against the built-in AI and produce a comparison.\n", "\n", - " matchups = list(itertools.permutations(enumerate(agent_names), 2))\n", - " total_matchups = len(matchups)\n", + " Args:\n", + " env: Gymnasium ALE/Tennis-v5 environment.\n", + " agents: Dictionary mapping agent names to Agent instances.\n", + " episodes_per_agent: Number of evaluation episodes per agent.\n", "\n", - " for idx, ((idx1, name1), (idx2, name2)) in enumerate(matchups, start=1):\n", - " print(f\"[Evaluation {idx}/{total_matchups}] Match : {name1} vs {name2}\")\n", + " Returns:\n", + " Dict mapping agent names to their evaluation results.\n", "\n", - " agent1 = agents[name1]\n", - " agent2 = agents[name2]\n", + " \"\"\"\n", + " results: dict[str, dict[str, object]] = {}\n", + " n_agents = len(agents)\n", "\n", - " wins, _ = run_tournament_match(\n", - " env=env,\n", - " agent1=agent1,\n", - " agent2=agent2,\n", - " episodes=episodes_per_match,\n", - " train_mode=False,\n", - " epsilon_start=0.0,\n", + " for idx, (name, agent) in enumerate(agents.items(), start=1):\n", + " print(f\"[Evaluation {idx}/{n_agents}] {name}\")\n", + " results[name] = evaluate_agent(\n", + " env, agent, name, episodes=episodes_per_agent,\n", " )\n", + " mean_r = results[name][\"mean_reward\"]\n", + " wr = results[name][\"win_rate\"]\n", + " print(f\" -> Mean reward: {mean_r:.2f} | Win rate: {wr:.1%}\\n\")\n", "\n", - " total_games = wins[\"first_0\"] + wins[\"second_0\"]\n", - " if total_games > 0:\n", - " win_matrix[idx1, idx2] = wins[\"first_0\"] / total_games\n", - "\n", - " print(f\"-> Résultat : {name1} {wins['first_0']} - {wins['second_0']} {name2}\\n\")\n", - "\n", - " return win_matrix, agent_names\n" + " return results\n" ] }, { - "cell_type": "code", - "execution_count": 11, - "id": "1dc5f3f5", + "cell_type": "markdown", + "id": "9605e9c4", "metadata": {}, - "outputs": [], "source": [ - "env = create_env(obs_type=\"ram\")\n", - "env.reset()\n", + "## Agent Instantiation & Incremental Training (One Agent at a Time)\n", "\n", - "obs_space = env.observation_space(\"first_0\")\n", - "n_actions = env.action_space(\"first_0\").n\n", - "n_features = int(np.prod(obs_space.shape))\n", + "**Environment**: `ALE/Tennis-v5` (grayscale, 84×84×4 frames → 28,224 features, 18 actions).\n", "\n", - "agent_random = RandomAgent(seed=42, action_space=n_actions)\n", - "agent_q = LinearAgent(n_features=n_features, n_actions=n_actions, method=\"q_learning\")\n", - "agent_sarsa = LinearAgent(n_features=n_features, n_actions=n_actions, method=\"sarsa\")\n", - "agent_dqn = DQNAgent(n_features=n_features, n_actions=n_actions)\n", - "agent_mc = MonteCarloAgent(n_features=n_features, n_actions=n_actions)\n", + "**Agents**:\n", + "- **Random** — random baseline (no training needed)\n", + "- **SARSA** — linear approximation, semi-gradient TD(0)\n", + "- **Q-Learning** — linear approximation, off-policy\n", + "- **DQN** — NumPy MLP (2 hidden layers of 128, experience replay, target network)\n", + "- **Monte Carlo** — linear approximation, first-visit returns\n", "\n", - "agents_dict = {\n", - " \"Random\": agent_random,\n", - " \"Q-Learning\": agent_q,\n", - " \"SARSA\": agent_sarsa,\n", - " \"DQN\": agent_dqn,\n", - " \"MC\": agent_mc,\n", - "}\n" + "**Workflow**:\n", + "1. Train **one** selected agent (`AGENT_TO_TRAIN`)\n", + "2. Save its weights to `checkpoints/.pkl`\n", + "3. Repeat later for another agent without retraining previous ones\n", + "4. Load all saved checkpoints before the final evaluation" ] }, { "cell_type": "code", - "execution_count": null, - "id": "afbdf033", + "execution_count": 28, + "id": "6f6ba8df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1/20] Début entraînement : Random vs Q-Learning\n" + "Observation shape : (4, 84, 84)\n", + "Feature vector dim: 28224\n", + "Number of actions : 18\n" ] } ], "source": [ - "matchups = list(itertools.permutations(agents_dict.keys(), 2))\n", - "total_matchups = len(matchups)\n", + "# Create environment\n", + "env = create_env()\n", + "obs, _info = env.reset()\n", "\n", - "for idx, (name1, name2) in enumerate(matchups, start=1):\n", - " print(f\"[{idx}/{total_matchups}] Début entraînement : {name1} vs {name2}\")\n", + "n_actions = int(env.action_space.n)\n", + "n_features = int(np.prod(obs.shape))\n", "\n", - " wins, rewards_history = run_tournament_match(\n", - " env=env,\n", - " agent1=agents_dict[name1],\n", - " agent2=agents_dict[name2],\n", - " episodes=1000,\n", - " train_mode=True,\n", - " epsilon_start=0.2,\n", - " )\n", + "print(f\"Observation shape : {obs.shape}\")\n", + "print(f\"Feature vector dim: {n_features}\")\n", + "print(f\"Number of actions : {n_actions}\")\n", "\n", - " plot_learning_curves(rewards_history, window=10)\n", + "# Instantiate agents\n", + "agent_random = RandomAgent(seed=42, action_space=int(n_actions))\n", + "agent_sarsa = SarsaAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n", + "agent_q = QLearningAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n", + "agent_dqn = DQNAgent(n_features=n_features, n_actions=n_actions, lr=1e-4)\n", + "agent_mc = MonteCarloAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n", "\n", - " print(f\"[{idx}/{total_matchups}] Fin entraînement : {name1} vs {name2}\")\n", - " if idx < total_matchups:\n", - " print(\"→ Passage au match suivant...\\n\")\n", - " else:\n", - " print(\"→ Tous les matchs d'entraînement sont terminés.\\n\")\n", - "\n", - "win_matrix, agent_names = evaluate_tournament(env, agents_dict, episodes_per_match=20)\n", - "plot_win_rate_matrix(win_matrix, agent_names)\n" + "agents = {\n", + " \"Random\": agent_random,\n", + " \"SARSA\": agent_sarsa,\n", + " \"Q-Learning\": agent_q,\n", + " \"DQN\": agent_dqn,\n", + " \"Monte Carlo\": agent_mc,\n", + "}\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "9555e5b1", + "id": "4d449701", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected agent: SARSA\n", + "Checkpoint path: checkpoints/sarsa.pkl\n", + "\n", + "============================================================\n", + "Training: SARSA (5000 episodes)\n", + "============================================================\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected agent: SARSA\n", + "Checkpoint path: checkpoints/sarsa.pkl\n", + "\n", + "============================================================\n", + "Training: SARSA (5000 episodes)\n", + "============================================================\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2809e1c28b15458daaeba87066ad1c74", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Training SARSA: 0%| | 0/5000 [00:00 weights loaded, training skipped.\")\n", + " training_histories[AGENT_TO_TRAIN] = []\n", + "else:\n", + " print(f\"\\n{'='*60}\")\n", + " print(f\"Training: {AGENT_TO_TRAIN} ({TRAINING_EPISODES} episodes)\")\n", + " print(f\"{'='*60}\")\n", + "\n", + " training_histories[AGENT_TO_TRAIN] = train_agent(\n", + " env=env,\n", + " agent=agent,\n", + " name=AGENT_TO_TRAIN,\n", + " episodes=TRAINING_EPISODES,\n", + " epsilon_start=1.0,\n", + " epsilon_end=0.05,\n", + " epsilon_decay=0.999,\n", + " )\n", + "\n", + " avg_last_100 = np.mean(training_histories[AGENT_TO_TRAIN][-100:])\n", + " print(f\"-> {AGENT_TO_TRAIN} avg reward (last 100 eps): {avg_last_100:.2f}\")\n", + "\n", + " agent.save(str(ckpt_path))\n", + " print(\"Checkpoint saved.\")\n", + "\n", + "if training_histories.get(AGENT_TO_TRAIN):\n", + " plot_training_curves(training_histories, window=100)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0fc0c643", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# Load all available checkpoints before final evaluation\n", + "CHECKPOINT_DIR = Path(\"checkpoints\")\n", + "loaded_agents: list[str] = []\n", + "missing_agents: list[str] = []\n", + "\n", + "for name, agent in agents.items():\n", + " if name == \"Random\":\n", + " continue\n", + "\n", + " ckpt_name = name.lower().replace(\" \", \"_\").replace(\"-\", \"_\") + \".pkl\"\n", + " ckpt_path = CHECKPOINT_DIR / ckpt_name\n", + "\n", + " if ckpt_path.exists():\n", + " agent.load(str(ckpt_path))\n", + " loaded_agents.append(name)\n", + " else:\n", + " missing_agents.append(name)\n", + "\n", + "print(f\"Loaded checkpoints: {loaded_agents}\")\n", + "if missing_agents:\n", + " print(f\"Missing checkpoints (untrained/not saved yet): {missing_agents}\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "0e5f2c49", + "metadata": {}, + "source": [ + "## Final Evaluation\n", + "\n", + "Each agent plays 20 episodes against the built-in AI with no exploration (ε = 0).\n", + "Performance is compared via mean reward and win rate." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "70f5d5cd", + "metadata": {}, + "outputs": [], + "source": [ + "# Build evaluation set: Random + agents with existing checkpoints\n", + "eval_agents: dict[str, Agent] = {\"Random\": agents[\"Random\"]}\n", + "missing_agents: list[str] = []\n", + "\n", + "for name, agent in agents.items():\n", + " if name == \"Random\":\n", + " continue\n", + "\n", + " ckpt_name = name.lower().replace(\" \", \"_\").replace(\"-\", \"_\") + \".pkl\"\n", + " ckpt_path = CHECKPOINT_DIR / ckpt_name\n", + "\n", + " if ckpt_path.exists():\n", + " agent.load(str(ckpt_path))\n", + " eval_agents[name] = agent\n", + " else:\n", + " missing_agents.append(name)\n", + "\n", + "print(f\"Agents evaluated: {list(eval_agents.keys())}\")\n", + "if missing_agents:\n", + " print(f\"Skipped (no checkpoint yet): {missing_agents}\")\n", + "\n", + "if len(eval_agents) < 2:\n", + " raise RuntimeError(\"Train at least one non-random agent before final evaluation.\")\n", + "\n", + "results = evaluate_tournament(env, eval_agents, episodes_per_agent=20)\n", + "plot_evaluation_comparison(results)\n", + "\n", + "# Print summary table\n", + "print(f\"\\n{'Agent':<15} {'Mean Reward':>12} {'Std':>8} {'Win Rate':>10}\")\n", + "print(\"-\" * 48)\n", + "for name, res in results.items():\n", + " print(f\"{name:<15} {res['mean_reward']:>12.2f} {res['std_reward']:>8.2f} {res['win_rate']:>9.1%}\")\n" + ] } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index 81f5a57..6a8de78 100644 --- a/pyproject.toml 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