Reinforcement Learning – An Introduction, 2nd Edition

• Authors: Richard S. Sutton and Andrew G. Barto
• Comprehensive coverage of reinforcement learning.
• Clear explanations and illustrative examples.
• Theoretical foundations and practical algorithms.
• Explores Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, policy gradient methods, and deep reinforcement learning.
• Suitable for students, researchers, and practitioners.

Reinforcement Learning: An Introduction, Second Edition, by Richard S. Sutton and Andrew G. Barto, is a comprehensive guide to reinforcement learning (RL). This updated edition incorporates recent advancements, especially in deep reinforcement learning. It provides a clear introduction to RL concepts and algorithms, covering Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, and policy gradient methods. Essential reading for anyone seeking a solid foundation in reinforcement learning.