Deep Reinforcement Learning Hands-On - Third Edition
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Deep Reinforcement Learning Hands-On - Third Edition

A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF

About the Book

Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers.

The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.

If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. What Is Reinforcement Learning?
  2. OpenAI Gym API and Gymnasium
  3. Deep Learning with PyTorch
  4. The Cross-Entropy Method
  5. Tabular Learning and the Bellman Equation
  6. Deep Q-Networks
  7. Higher-Level RL Libraries
  8. DQN Extensions
  9. Ways to Speed Up RL
  10. Stocks Trading Using RL
  11. Policy Gradients
  12. Actor-Critic Methods - A2C and A3C
  13. The TextWorld Environment
  14. Web Navigation
  15. Continuous Action Space
  16. Trust Region Methods
  17. Black-Box Optimizations in RL
  18. Advanced Exploration
  19. Reinforcement Learning with Human Feedback
  20. AlphaGo Zero and MuZero
  21. RL in Discrete Optimization
  22. Multi-Agent RL

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