Reward yourself and take this journey into RL 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.

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 RL agents in web environments, 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.

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Spis treści

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 eBook -- spis treści

• 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