Reinforcement Learning Explained

This book provides a rigorous yet accessible foundation in reinforcement learning, covering mathematical principles and practical methods. It introduces MDPs, value functions, Bellman equations, and classical solution methods including dynamic programming, Monte Carlo, and Q-learning, with Python implementations.