The Best Books on Reinforcement Learning

Reinforcement learning is a fascinating field that differs from supervised learning in key ways, such as the absence of labels for every input and the dependency of inputs on the learning process. This curated list of books provides a comprehensive introduction to reinforcement learning, starting with the canonical text 'Reinforcement Learning: An Introduction' by Richard S. Sutton and Andrew G. Barto. This book is highly applied and approachable, featuring in-text exercises and Python code for practical experimentation. The new edition includes up-to-date examples of reinforcement learning that have been prominent in the news. Additionally, 'Decision Making Under Uncertainty' by Mykel J. Kochenderfer offers a broader perspective on probabilistic decision-making, making it a valuable resource for those interested in the general principles of decision-making under uncertainty. Both books come with supplementary materials like errata, problems, and solutions, ensuring a well-rounded learning experience.