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This repository is a reinforcement learning library implementing algorithms from Sutton & Barto's 'Reinforcement Learning: An Introduction'. It provides a modular framework for model-free and model-based RL methods, including multi-armed bandits, Monte Carlo methods, temporal difference learning, planning algorithms (Dyna-Q, MCTS), and function approximation. The code is organized into core interfaces (rl/utils.py), model-free/model-based frameworks, solver modules, and examples demonstrating usage. While not production-ready, it serves as an educational resource and reference implementation of classic RL algorithms.
RATING:
MAINT: maintained
DRIFT: low