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Arthur DANJOU
63ebb3ec8d
- Updated weight and feature storage to use float32 for reduced memory bandwidth. - Implemented compact storage for raw observations as uint8, batch-normalized at episode end. - Introduced vectorized return computation and chunk-based weight updates using einsum. - Reduced weight sanitization to once per episode instead of per-step. - Refactored action selection and return calculation for improved efficiency.
1364 lines
57 KiB
Plaintext
1364 lines
57 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "fef45687",
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"metadata": {},
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"source": [
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"# RL Project: Atari Tennis Tournament\n",
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"\n",
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"This notebook implements four Reinforcement Learning algorithms to play Atari Tennis (`ALE/Tennis-v5` via Gymnasium):\n",
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"\n",
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"1. **SARSA** — Semi-gradient SARSA with linear approximation (inspired by Lab 7, on-policy update from Lab 5B)\n",
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"2. **Q-Learning** — Off-policy linear approximation (inspired by Lab 5B)\n",
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"3. **Monte Carlo** — First-visit MC control with linear approximation (inspired by Lab 4)\n",
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"\n",
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"Each agent is **pre-trained independently** against the built-in Atari AI opponent, then evaluated in a comparative tournament."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b50d7174",
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"metadata": {},
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"outputs": [],
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"source": [
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"import itertools\n",
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"import pickle\n",
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"from pathlib import Path\n",
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"\n",
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"import ale_py # noqa: F401 — registers ALE environments\n",
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"import gymnasium as gym\n",
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"import supersuit as ss\n",
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"from gymnasium.wrappers import FrameStackObservation, ResizeObservation\n",
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"from pettingzoo.atari import tennis_v3\n",
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"from tqdm.auto import tqdm\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import seaborn as sns\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ff3486a4",
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"metadata": {},
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"outputs": [],
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"source": [
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"CHECKPOINT_DIR = Path(\"checkpoints\")\n",
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"CHECKPOINT_DIR.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"\n",
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"def get_path(name: str) -> Path:\n",
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" \"\"\"Return the checkpoint path for an agent (.pkl).\"\"\"\n",
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" base = name.lower().replace(\" \", \"_\").replace(\"-\", \"_\")\n",
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" return CHECKPOINT_DIR / (base + \".pkl\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ec691487",
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"metadata": {},
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"source": [
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"# Utility Functions\n",
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"\n",
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"## Observation Normalization\n",
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"\n",
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"The Tennis environment produces image observations of shape `(4, 84, 84)` after preprocessing (grayscale + resize + frame stack).\n",
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"We normalize them into 1D `float64` vectors divided by 255, as in Lab 7 (continuous feature normalization).\n",
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"\n",
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"## ε-greedy Policy\n",
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"\n",
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"Follows the pattern from Lab 5B (`epsilon_greedy`) and Lab 7 (`epsilon_greedy_action`):\n",
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"- With probability ε: random action (exploration)\n",
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"- With probability 1−ε: action maximizing $\\hat{q}(s, a)$ with uniform tie-breaking (`np.flatnonzero`)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "be85c130",
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"metadata": {},
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"outputs": [],
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"source": [
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"def normalize_obs(observation: np.ndarray) -> np.ndarray:\n",
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" \"\"\"Flatten and normalize an observation to a 1D float64 vector.\n",
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"\n",
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" Replicates the /255.0 normalization used in all agents from the original project.\n",
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" For image observations of shape (4, 84, 84), this produces a vector of length 28_224.\n",
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"\n",
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" Args:\n",
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" observation: Raw observation array from the environment.\n",
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"\n",
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" Returns:\n",
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" 1D numpy array of dtype float64, values in [0, 1].\n",
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"\n",
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" \"\"\"\n",
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" return observation.flatten().astype(np.float64) / 255.0\n",
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"\n",
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"\n",
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"def epsilon_greedy(\n",
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" q_values: np.ndarray,\n",
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" epsilon: float,\n",
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" rng: np.random.Generator,\n",
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") -> int:\n",
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" \"\"\"Select an action using an ε-greedy policy with fair tie-breaking.\n",
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"\n",
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" Follows the same logic as Lab 5B epsilon_greedy and Lab 7 epsilon_greedy_action:\n",
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" - With probability epsilon: choose a random action (exploration).\n",
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" - With probability 1-epsilon: choose the action with highest Q-value (exploitation).\n",
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" - If multiple actions share the maximum Q-value, break ties uniformly at random.\n",
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"\n",
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" Handles edge cases: empty q_values, NaN/Inf values.\n",
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"\n",
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" Args:\n",
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" q_values: Array of Q-values for each action, shape (n_actions,).\n",
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" epsilon: Exploration probability in [0, 1].\n",
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" rng: NumPy random number generator.\n",
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"\n",
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" Returns:\n",
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" Selected action index.\n",
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"\n",
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" \"\"\"\n",
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" q_values = np.asarray(q_values, dtype=np.float64).reshape(-1)\n",
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"\n",
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" if q_values.size == 0:\n",
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" msg = \"q_values is empty.\"\n",
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" raise ValueError(msg)\n",
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"\n",
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" if rng.random() < epsilon:\n",
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" return int(rng.integers(0, q_values.size))\n",
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"\n",
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" # Handle NaN/Inf values safely\n",
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" finite_mask = np.isfinite(q_values)\n",
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" if not np.any(finite_mask):\n",
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" return int(rng.integers(0, q_values.size))\n",
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"\n",
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" safe_q = q_values.copy()\n",
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" safe_q[~finite_mask] = -np.inf\n",
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" max_val = np.max(safe_q)\n",
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" best = np.flatnonzero(safe_q == max_val)\n",
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"\n",
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" if best.size == 0:\n",
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" return int(rng.integers(0, q_values.size))\n",
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"\n",
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" return int(rng.choice(best))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb53da28",
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"metadata": {},
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"source": [
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"# Agent Definitions\n",
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"\n",
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"## Base Class `Agent`\n",
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"\n",
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"Common interface for all agents, same signatures: `get_action`, `update`, `save`, `load`.\n",
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"Serialization uses `pickle` (compatible with numpy arrays)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "ded9b1fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"class Agent:\n",
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" \"\"\"Base class for reinforcement learning agents.\n",
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"\n",
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" All agents share this interface so they are compatible with the tournament system.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(self, seed: int, action_space: int) -> None:\n",
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" \"\"\"Initialize the agent with its action space and a reproducible RNG.\"\"\"\n",
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" self.action_space = action_space\n",
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" self.rng = np.random.default_rng(seed=seed)\n",
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"\n",
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" def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
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" \"\"\"Select an action from the current observation.\"\"\"\n",
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" raise NotImplementedError\n",
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"\n",
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" def update(\n",
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" self,\n",
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" state: np.ndarray,\n",
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" action: int,\n",
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" reward: float,\n",
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" next_state: np.ndarray,\n",
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" done: bool,\n",
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" next_action: int | None = None,\n",
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" ) -> None:\n",
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" \"\"\"Update agent parameters from one transition.\"\"\"\n",
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"\n",
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" def save(self, filename: str) -> None:\n",
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" \"\"\"Save the agent state to disk using pickle.\"\"\"\n",
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" with Path(filename).open(\"wb\") as f:\n",
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" pickle.dump(self.__dict__, f)\n",
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"\n",
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" def load(self, filename: str) -> None:\n",
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" \"\"\"Load the agent state from disk.\"\"\"\n",
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" with Path(filename).open(\"rb\") as f:\n",
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" self.__dict__.update(pickle.load(f)) # noqa: S301\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8a4eae79",
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"metadata": {},
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"source": [
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"## Random Agent (baseline)\n",
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"\n",
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"Serves as a reference to evaluate the performance of learning agents."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "78bdc9d2",
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"metadata": {},
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"outputs": [],
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"source": [
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"class RandomAgent(Agent):\n",
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" \"\"\"A simple agent that selects actions uniformly at random (baseline).\"\"\"\n",
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"\n",
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" def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
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" \"\"\"Select a random action, ignoring the observation and epsilon.\"\"\"\n",
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" _ = observation, epsilon\n",
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" return int(self.rng.integers(0, self.action_space))\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f679032",
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"metadata": {},
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"source": [
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"## SARSA Agent — Linear Approximation (Semi-gradient)\n",
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"\n",
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"This agent combines:\n",
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"- **Linear approximation** from Lab 7 (`SarsaAgent`): $\\hat{q}(s, a; \\mathbf{W}) = \\mathbf{W}_a^\\top \\phi(s)$\n",
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"- **On-policy SARSA update** from Lab 5B (`train_sarsa`): $\\delta = r + \\gamma \\hat{q}(s', a') - \\hat{q}(s, a)$\n",
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"\n",
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"The semi-gradient update rule is:\n",
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"$$W_a \\leftarrow W_a + \\alpha \\cdot \\delta \\cdot \\phi(s)$$\n",
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"\n",
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"where $\\phi(s)$ is the normalized observation vector (analogous to tile coding features in Lab 7, but in dense form)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "c124ed9a",
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"metadata": {},
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"outputs": [],
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"source": [
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"class SarsaAgent(Agent):\n",
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" \"\"\"Semi-gradient SARSA agent with linear function approximation.\n",
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"\n",
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" Inspired by:\n",
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" - Lab 7 SarsaAgent: linear q(s,a) = W_a . phi(s), semi-gradient update\n",
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" - Lab 5B train_sarsa: on-policy TD target using Q(s', a')\n",
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"\n",
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" The weight matrix W has shape (n_actions, n_features).\n",
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" For a given state s, q(s, a) = W[a] @ phi(s) is the dot product\n",
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" of the action's weight row with the normalized observation.\n",
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" \"\"\"\n",
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"\n",
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" def __init__(\n",
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" self,\n",
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" n_features: int,\n",
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" n_actions: int,\n",
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" alpha: float = 0.001,\n",
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" gamma: float = 0.99,\n",
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" seed: int = 42,\n",
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" ) -> None:\n",
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" \"\"\"Initialize SARSA agent with linear weights.\n",
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"\n",
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" Args:\n",
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" n_features: Dimension of the feature vector phi(s).\n",
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" n_actions: Number of discrete actions.\n",
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" alpha: Learning rate (kept small for high-dim features).\n",
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" gamma: Discount factor.\n",
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" seed: RNG seed for reproducibility.\n",
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"\n",
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" \"\"\"\n",
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" super().__init__(seed, n_actions)\n",
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" self.n_features = n_features\n",
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" self.alpha = alpha\n",
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" self.gamma = gamma\n",
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" # Weight matrix: one row per action, analogous to Lab 7's self.w\n",
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" # but organized as (n_actions, n_features) for dense features.\n",
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" self.W = np.zeros((n_actions, n_features), dtype=np.float64)\n",
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"\n",
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" def _q_values(self, phi: np.ndarray) -> np.ndarray:\n",
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" \"\"\"Compute Q-values for all actions given feature vector phi(s).\n",
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"\n",
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" Equivalent to Lab 7's self.q(s, a) = self.w[idx].sum()\n",
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" but using dense linear approximation: q(s, a) = W[a] @ phi.\n",
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"\n",
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" Args:\n",
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" phi: Normalized feature vector, shape (n_features,).\n",
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"\n",
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" Returns:\n",
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" Array of Q-values, shape (n_actions,).\n",
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"\n",
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" \"\"\"\n",
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" return self.W @ phi # shape (n_actions,)\n",
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"\n",
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" def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
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" \"\"\"Select action using ε-greedy policy over linear Q-values.\n",
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"\n",
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" Same pattern as Lab 7 SarsaAgent.eps_greedy:\n",
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" compute q-values for all actions, then apply epsilon_greedy.\n",
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" \"\"\"\n",
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" phi = normalize_obs(observation)\n",
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" q_vals = self._q_values(phi)\n",
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" return epsilon_greedy(q_vals, epsilon, self.rng)\n",
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"\n",
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" def update(\n",
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" self,\n",
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" state: np.ndarray,\n",
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" action: int,\n",
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" reward: float,\n",
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" next_state: np.ndarray,\n",
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" done: bool,\n",
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" next_action: int | None = None,\n",
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" ) -> None:\n",
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" \"\"\"Perform one semi-gradient SARSA update.\n",
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"\n",
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" Follows the SARSA update from Lab 5B train_sarsa:\n",
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" td_target = r + gamma * Q(s', a') * (0 if done else 1)\n",
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" Q(s, a) += alpha * (td_target - Q(s, a))\n",
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"\n",
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" In continuous form with linear approximation (Lab 7 SarsaAgent.update):\n",
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" delta = target - q(s, a)\n",
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" W[a] += alpha * delta * phi(s)\n",
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"\n",
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" Args:\n",
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" state: Current observation.\n",
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" action: Action taken.\n",
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" reward: Reward received.\n",
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" next_state: Next observation.\n",
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" done: Whether the episode ended.\n",
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" next_action: Action chosen in next state (required for SARSA).\n",
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"\n",
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" \"\"\"\n",
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" phi = np.nan_to_num(normalize_obs(state), nan=0.0, posinf=0.0, neginf=0.0)\n",
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" q_sa = float(self.W[action] @ phi) # current estimate q(s, a)\n",
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" if not np.isfinite(q_sa):\n",
|
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" q_sa = 0.0\n",
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"\n",
|
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" if done:\n",
|
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" # Terminal: no future value (Lab 5B: gamma * Q[s2, a2] * 0)\n",
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" target = reward\n",
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" else:\n",
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" # On-policy: use q(s', a') where a' is the actual next action\n",
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" # This is the key SARSA property (Lab 5B)\n",
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" phi_next = np.nan_to_num(normalize_obs(next_state), nan=0.0, posinf=0.0, neginf=0.0)\n",
|
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" if next_action is None:\n",
|
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" next_action = 0 # fallback, should not happen in practice\n",
|
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" q_sp_ap = float(self.W[next_action] @ phi_next)\n",
|
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" if not np.isfinite(q_sp_ap):\n",
|
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" q_sp_ap = 0.0\n",
|
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" target = float(reward) + self.gamma * q_sp_ap\n",
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"\n",
|
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" # Semi-gradient update: W[a] += alpha * delta * phi(s)\n",
|
||
" # Analogous to Lab 7: self.w[idx] += self.alpha * delta\n",
|
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" if not np.isfinite(target):\n",
|
||
" return\n",
|
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"\n",
|
||
" delta = float(target - q_sa)\n",
|
||
" if not np.isfinite(delta):\n",
|
||
" return\n",
|
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"\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": "d4e18536",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 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": 7,
|
||
"id": "f5b5b9ea",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"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": "b7b63455",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 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": 8,
|
||
"id": "3c9d74be",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"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",
|
||
" Performance optimizations over naive per-step implementation:\n",
|
||
" - float32 weights & features (halves memory bandwidth, faster SIMD)\n",
|
||
" - Raw observations stored compactly as uint8, batch-normalized at episode end\n",
|
||
" - Vectorized return computation & chunk-based weight updates via einsum\n",
|
||
" - Single weight sanitization per episode instead of per-step\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",
|
||
" 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.float32)\n",
|
||
" self._obs_buf: list[np.ndarray] = []\n",
|
||
" self._act_buf: list[int] = []\n",
|
||
" self._rew_buf: list[float] = []\n",
|
||
"\n",
|
||
" def _q_values(self, phi: np.ndarray) -> np.ndarray:\n",
|
||
" return self.W @ phi\n",
|
||
"\n",
|
||
" def get_action(self, observation: np.ndarray, epsilon: float = 0.0) -> int:\n",
|
||
" phi = observation.flatten().astype(np.float32) / np.float32(255.0)\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",
|
||
" _ = next_state, next_action\n",
|
||
"\n",
|
||
" self._obs_buf.append(state)\n",
|
||
" self._act_buf.append(action)\n",
|
||
" self._rew_buf.append(reward)\n",
|
||
"\n",
|
||
" if not done:\n",
|
||
" return\n",
|
||
"\n",
|
||
" n = len(self._rew_buf)\n",
|
||
" actions = np.array(self._act_buf, dtype=np.intp)\n",
|
||
"\n",
|
||
" returns = np.empty(n, dtype=np.float32)\n",
|
||
" G = np.float32(0.0)\n",
|
||
" gamma32 = np.float32(self.gamma)\n",
|
||
" for i in range(n - 1, -1, -1):\n",
|
||
" G = gamma32 * G + np.float32(self._rew_buf[i])\n",
|
||
" returns[i] = G\n",
|
||
"\n",
|
||
" alpha32 = np.float32(self.alpha)\n",
|
||
" chunk_size = 500\n",
|
||
" for start in range(0, n, chunk_size):\n",
|
||
" end = min(start + chunk_size, n)\n",
|
||
" cs = end - start\n",
|
||
"\n",
|
||
" raw = np.array(self._obs_buf[start:end])\n",
|
||
" phi = raw.reshape(cs, -1).astype(np.float32)\n",
|
||
" phi /= np.float32(255.0)\n",
|
||
"\n",
|
||
" ca = actions[start:end]\n",
|
||
" q_sa = np.einsum(\"ij,ij->i\", self.W[ca], phi)\n",
|
||
"\n",
|
||
" deltas = np.clip(returns[start:end] - q_sa, -1000.0, 1000.0)\n",
|
||
"\n",
|
||
" for a in range(self.action_space):\n",
|
||
" mask = ca == a\n",
|
||
" if not np.any(mask):\n",
|
||
" continue\n",
|
||
" self.W[a] += alpha32 * (deltas[mask] @ phi[mask])\n",
|
||
"\n",
|
||
" self.W = np.nan_to_num(self.W, nan=0.0, posinf=1e6, neginf=-1e6)\n",
|
||
"\n",
|
||
" self._obs_buf.clear()\n",
|
||
" self._act_buf.clear()\n",
|
||
" self._rew_buf.clear()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "91e51dc8",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 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": 9,
|
||
"id": "f9a973dd",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"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": "18cb28d8",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 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": 10,
|
||
"id": "06b91580",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def train_agent(\n",
|
||
" env: gym.Env,\n",
|
||
" agent: Agent,\n",
|
||
" name: str,\n",
|
||
" *,\n",
|
||
" episodes: int = 5000,\n",
|
||
" epsilon_start: float = 1.0,\n",
|
||
" epsilon_end: float = 0.05,\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",
|
||
" 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",
|
||
" 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",
|
||
" Returns:\n",
|
||
" List of total rewards per episode.\n",
|
||
"\n",
|
||
" \"\"\"\n",
|
||
" rewards_history: list[float] = []\n",
|
||
" epsilon = epsilon_start\n",
|
||
"\n",
|
||
" pbar = tqdm(range(episodes), desc=f\"Training {name}\", leave=True)\n",
|
||
"\n",
|
||
" for _ep in pbar:\n",
|
||
" obs, _info = env.reset()\n",
|
||
" obs = np.asarray(obs)\n",
|
||
" total_reward = 0.0\n",
|
||
"\n",
|
||
" # Select first action\n",
|
||
" action = agent.get_action(obs, epsilon=epsilon)\n",
|
||
"\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",
|
||
" # 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",
|
||
" # 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",
|
||
" obs = next_obs\n",
|
||
" action = next_action\n",
|
||
"\n",
|
||
" rewards_history.append(total_reward)\n",
|
||
" epsilon = max(epsilon_end, epsilon * epsilon_decay)\n",
|
||
"\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 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",
|
||
" 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",
|
||
" path: str,\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",
|
||
" path: File path to save the plot image.\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=name)\n",
|
||
" else:\n",
|
||
" plt.plot(rewards, label=f\"{name} (raw)\")\n",
|
||
"\n",
|
||
" plt.xlabel(\"Episodes\")\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.savefig(path)\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 evaluate_tournament(\n",
|
||
" env: gym.Env,\n",
|
||
" agents: dict[str, Agent],\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",
|
||
" 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",
|
||
" Returns:\n",
|
||
" Dict mapping agent names to their evaluation results.\n",
|
||
"\n",
|
||
" \"\"\"\n",
|
||
" results: dict[str, dict[str, object]] = {}\n",
|
||
" n_agents = len(agents)\n",
|
||
"\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",
|
||
" return results\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9605e9c4",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Agent Instantiation & Incremental Training (One Agent at a Time)\n",
|
||
"\n",
|
||
"**Environment**: `ALE/Tennis-v5` (grayscale, 84×84×4 frames → 28,224 features, 18 actions).\n",
|
||
"\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",
|
||
"- **Monte Carlo** — linear approximation, first-visit returns\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": 11,
|
||
"id": "6f6ba8df",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"A.L.E: Arcade Learning Environment (version 0.11.2+ecc1138)\n",
|
||
"[Powered by Stella]\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Observation shape : (4, 84, 84)\n",
|
||
"Feature vector dim: 28224\n",
|
||
"Number of actions : 18\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"objc[49878]: Class SDLApplication is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d2c8) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418890). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLAppDelegate is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d318) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x1244188e0). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLTranslatorResponder is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d390) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418958). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLMessageBoxPresenter is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d3b8) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418980). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_cocoametalview is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d408) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x1244189d0). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLOpenGLContext is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d458) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418a20). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_ShapeData is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d4d0) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418a98). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_CocoaClosure is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d520) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418ae8). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_VideoData is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d570) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418b38). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_WindowData is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d5c0) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418b88). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLWindow is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d5e8) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418bb0). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class Cocoa_WindowListener is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d610) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418bd8). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDLView is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d688) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418c50). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class METAL_RenderData is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d700) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418cc8). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class METAL_TextureData is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d750) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418d18). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_RumbleMotor is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d778) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418d40). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n",
|
||
"objc[49878]: Class SDL_RumbleContext is implemented in both /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/pygame/.dylibs/libSDL2-2.0.0.dylib (0x11118d7c8) and /Users/arthurdanjou/Workspace/studies/.venv/lib/python3.13/site-packages/cv2/.dylibs/libSDL2-2.0.0.dylib (0x124418d90). This may cause spurious casting failures and mysterious crashes. One of the duplicates must be removed or renamed.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Create environment\n",
|
||
"env = create_env()\n",
|
||
"obs, _info = env.reset()\n",
|
||
"\n",
|
||
"n_actions = int(env.action_space.n)\n",
|
||
"n_features = int(np.prod(obs.shape))\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",
|
||
"# 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_mc = MonteCarloAgent(n_features=n_features, n_actions=n_actions, alpha=1e-5)\n",
|
||
"\n",
|
||
"agents = {\n",
|
||
" \"Random\": agent_random,\n",
|
||
" \"SARSA\": agent_sarsa,\n",
|
||
" \"Q-Learning\": agent_q,\n",
|
||
" \"Monte Carlo\": agent_mc,\n",
|
||
"}\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "4d449701",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Selected agent: Monte Carlo\n",
|
||
"Checkpoint path: checkpoints/monte_carlo.pkl\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Training: Monte Carlo (2500 episodes)\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "0d5d098d18014fe6b736683e0b8b2488",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Training Monte Carlo: 0%| | 0/2500 [00:00<?, ?it/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"AGENT_TO_TRAIN = \"Monte Carlo\" # TODO: change to: \"Q-Learning\", \"Monte Carlo\", \"Random\"\n",
|
||
"TRAINING_EPISODES = 2500\n",
|
||
"FORCE_RETRAIN = False\n",
|
||
"\n",
|
||
"if AGENT_TO_TRAIN not in agents:\n",
|
||
" msg = f\"Unknown agent '{AGENT_TO_TRAIN}'. Available: {list(agents)}\"\n",
|
||
" raise ValueError(msg)\n",
|
||
"\n",
|
||
"training_histories: dict[str, list[float]] = {}\n",
|
||
"agent = agents[AGENT_TO_TRAIN]\n",
|
||
"checkpoint_path = get_path(AGENT_TO_TRAIN)\n",
|
||
"\n",
|
||
"print(f\"Selected agent: {AGENT_TO_TRAIN}\")\n",
|
||
"print(f\"Checkpoint path: {checkpoint_path}\")\n",
|
||
"\n",
|
||
"if AGENT_TO_TRAIN == \"Random\":\n",
|
||
" print(\"Random is a baseline and is not trained.\")\n",
|
||
" training_histories[AGENT_TO_TRAIN] = []\n",
|
||
"elif checkpoint_path.exists() and not FORCE_RETRAIN:\n",
|
||
" agent.load(str(checkpoint_path))\n",
|
||
" print(\"Checkpoint found -> 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(checkpoint_path))\n",
|
||
" print(\"Checkpoint saved.\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "a13a65df",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"plot_training_curves(\n",
|
||
" training_histories, f\"plots/{AGENT_TO_TRAIN}_training_curves.png\", window=100,\n",
|
||
")\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",
|
||
" checkpoint_path = get_path(name)\n",
|
||
"\n",
|
||
" if checkpoint_path.exists():\n",
|
||
" agent.load(str(checkpoint_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"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "d04d37e0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"def create_tournament_env():\n",
|
||
" \"\"\"Create PettingZoo Tennis env with preprocessing compatible with our agents.\"\"\"\n",
|
||
" env = tennis_v3.env(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",
|
||
"\n",
|
||
"\n",
|
||
"def run_pz_match(\n",
|
||
" env,\n",
|
||
" agent_first: Agent,\n",
|
||
" agent_second: Agent,\n",
|
||
" episodes: int = 10,\n",
|
||
" max_steps: int = 4000,\n",
|
||
") -> dict[str, int]:\n",
|
||
" \"\"\"Run multiple PettingZoo episodes between two agents.\n",
|
||
"\n",
|
||
" Returns wins for global labels {'first': ..., 'second': ..., 'draw': ...}.\n",
|
||
" \"\"\"\n",
|
||
" wins = {\"first\": 0, \"second\": 0, \"draw\": 0}\n",
|
||
"\n",
|
||
" for _ep in range(episodes):\n",
|
||
" env.reset()\n",
|
||
" rewards = {\"first_0\": 0.0, \"second_0\": 0.0}\n",
|
||
"\n",
|
||
" for step_idx, agent_id in enumerate(env.agent_iter()):\n",
|
||
" obs, reward, termination, truncation, _info = env.last()\n",
|
||
" done = termination or truncation\n",
|
||
" rewards[agent_id] += float(reward)\n",
|
||
"\n",
|
||
" if done or step_idx >= max_steps:\n",
|
||
" action = None\n",
|
||
" else:\n",
|
||
" current_agent = agent_first if agent_id == \"first_0\" else agent_second\n",
|
||
" action = current_agent.get_action(np.asarray(obs), epsilon=0.0)\n",
|
||
"\n",
|
||
" env.step(action)\n",
|
||
"\n",
|
||
" if step_idx + 1 >= max_steps:\n",
|
||
" break\n",
|
||
"\n",
|
||
" if rewards[\"first_0\"] > rewards[\"second_0\"]:\n",
|
||
" wins[\"first\"] += 1\n",
|
||
" elif rewards[\"second_0\"] > rewards[\"first_0\"]:\n",
|
||
" wins[\"second\"] += 1\n",
|
||
" else:\n",
|
||
" wins[\"draw\"] += 1\n",
|
||
"\n",
|
||
" return wins\n",
|
||
"\n",
|
||
"\n",
|
||
"def run_pettingzoo_tournament(\n",
|
||
" agents: dict[str, Agent],\n",
|
||
" episodes_per_side: int = 10,\n",
|
||
") -> tuple[np.ndarray, list[str]]:\n",
|
||
" \"\"\"Round-robin tournament excluding Random, with seat-swap fairness.\"\"\"\n",
|
||
" _ = itertools # kept for notebook context consistency\n",
|
||
" candidate_names = [name for name in agents if name != \"Random\"]\n",
|
||
"\n",
|
||
" # Keep only agents that have a checkpoint\n",
|
||
" ready_names: list[str] = []\n",
|
||
" for name in candidate_names:\n",
|
||
" checkpoint_path = get_path(name)\n",
|
||
" if checkpoint_path.exists():\n",
|
||
" agents[name].load(str(checkpoint_path))\n",
|
||
" ready_names.append(name)\n",
|
||
"\n",
|
||
" if len(ready_names) < 2:\n",
|
||
" msg = \"Need at least 2 trained (checkpointed) non-random agents for PettingZoo tournament.\"\n",
|
||
" raise RuntimeError(msg)\n",
|
||
"\n",
|
||
" n = len(ready_names)\n",
|
||
" win_matrix = np.full((n, n), np.nan)\n",
|
||
" np.fill_diagonal(win_matrix, 0.5)\n",
|
||
"\n",
|
||
" for i in range(n):\n",
|
||
" for j in range(i + 1, n):\n",
|
||
" name_i = ready_names[i]\n",
|
||
" name_j = ready_names[j]\n",
|
||
"\n",
|
||
" print(f\"Matchup: {name_i} vs {name_j}\")\n",
|
||
" env = create_tournament_env()\n",
|
||
"\n",
|
||
" # Leg 1: i as first_0, j as second_0\n",
|
||
" leg1 = run_pz_match(\n",
|
||
" env,\n",
|
||
" agent_first=agents[name_i],\n",
|
||
" agent_second=agents[name_j],\n",
|
||
" episodes=episodes_per_side,\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Leg 2: swap seats\n",
|
||
" leg2 = run_pz_match(\n",
|
||
" env,\n",
|
||
" agent_first=agents[name_j],\n",
|
||
" agent_second=agents[name_i],\n",
|
||
" episodes=episodes_per_side,\n",
|
||
" )\n",
|
||
"\n",
|
||
" env.close()\n",
|
||
"\n",
|
||
" wins_i = leg1[\"first\"] + leg2[\"second\"]\n",
|
||
" wins_j = leg1[\"second\"] + leg2[\"first\"]\n",
|
||
"\n",
|
||
" decisive = wins_i + wins_j\n",
|
||
" if decisive == 0:\n",
|
||
" wr_i = 0.5\n",
|
||
" wr_j = 0.5\n",
|
||
" else:\n",
|
||
" wr_i = wins_i / decisive\n",
|
||
" wr_j = wins_j / decisive\n",
|
||
"\n",
|
||
" win_matrix[i, j] = wr_i\n",
|
||
" win_matrix[j, i] = wr_j\n",
|
||
"\n",
|
||
" print(f\" -> {name_i}: {wins_i} wins | {name_j}: {wins_j} wins\\n\")\n",
|
||
"\n",
|
||
" return win_matrix, ready_names\n",
|
||
"\n",
|
||
"\n",
|
||
"# Run tournament (non-random agents only)\n",
|
||
"win_matrix_pz, pz_names = run_pettingzoo_tournament(\n",
|
||
" agents=agents,\n",
|
||
" episodes_per_side=10,\n",
|
||
")\n",
|
||
"\n",
|
||
"# Plot win-rate matrix\n",
|
||
"plt.figure(figsize=(8, 6))\n",
|
||
"sns.heatmap(\n",
|
||
" win_matrix_pz,\n",
|
||
" annot=True,\n",
|
||
" fmt=\".2f\",\n",
|
||
" cmap=\"Blues\",\n",
|
||
" vmin=0.0,\n",
|
||
" vmax=1.0,\n",
|
||
" xticklabels=pz_names,\n",
|
||
" yticklabels=pz_names,\n",
|
||
")\n",
|
||
"plt.xlabel(\"Opponent\")\n",
|
||
"plt.ylabel(\"Agent\")\n",
|
||
"plt.title(\"PettingZoo Tournament Win Rate Matrix (Non-random agents)\")\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"# Rank agents by mean win rate vs others (excluding diagonal)\n",
|
||
"scores = {}\n",
|
||
"for idx, name in enumerate(pz_names):\n",
|
||
" row = np.delete(win_matrix_pz[idx], idx)\n",
|
||
" scores[name] = float(np.mean(row))\n",
|
||
"\n",
|
||
"ranking = sorted(scores.items(), key=lambda x: x[1], reverse=True)\n",
|
||
"print(\"Final ranking (PettingZoo tournament, non-random):\")\n",
|
||
"for rank_idx, (name, score) in enumerate(ranking, start=1):\n",
|
||
" print(f\"{rank_idx}. {name:<12} | mean win rate: {score:.3f}\")\n",
|
||
"\n",
|
||
"print(f\"\\nBest agent: {ranking[0][0]}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3f8b300d",
|
||
"metadata": {},
|
||
"source": [
|
||
"## PettingZoo Tournament (Agents vs Agents)\n",
|
||
"\n",
|
||
"This tournament uses `from pettingzoo.atari import tennis_v3` to make trained agents play against each other directly.\n",
|
||
"\n",
|
||
"- Checkpoints are loaded from `checkpoints/` (`.pkl`)\n",
|
||
"- `Random` is **excluded** from ranking\n",
|
||
"- Each pair plays in both seat positions (`first_0` and `second_0`) to reduce position bias\n",
|
||
"- A win-rate matrix and final ranking are produced"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "150e6764",
|
||
"metadata": {},
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "studies (3.13.9)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.13.9"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|