Mastering Reinforcement Learning with Python

Preface

Reinforcement Learning (RL) is a field of artificial intelligence used for creating self-learning autonomous agents. On a strong theoretical foundation, this book takes a pragmatic approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL.

Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agenst reinforcement learning. Then, the book will introduce you to some of the key approaches behind the most successful ...