Deep Reinforcement Learning Hands-On. A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF - Third Edition

Reward yourself and take this journey into reinforcement learning with the third edition of Deep Reinforcement Learning Hands-On. The book takes you through the basics of reinforcement learning to the latest use cases, including the use of reinforcement learning with a wide variety of applications, including discrete optimization, game playing, stock trading, and web browser navigation. This edition includes a new chapter about using reinforcement learning as part of LLMs’ training procedures.

The book retains its strengths by providing concise and easy-to-follow explanations. You’ll work through practical and diverse examples, from grid environments and games to stock trading and NLP chatbots, to give you a well-rounded understanding of reinforcement learning, its capabilities, and use cases. You’ll learn about key topics, such as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.

If you want to learn about RL using a practical approach with real-world applications, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition is your ideal companion.

This book will equip you with both the practical know-how of RL and the theoretical foundation to understand and implement most modern RL papers.