Deep Reinforcement Learning Hands-On - Third Edition
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
Table of Contents
- What Is Reinforcement Learning?
- OpenAI Gym API and Gymnasium
- Deep Learning with PyTorch
- The Cross-Entropy Method
- Tabular Learning and the Bellman Equation
- Deep Q-Networks
- Higher-Level RL Libraries
- DQN Extensions
- Ways to Speed Up RL
- Stocks Trading Using RL
- Policy Gradients
- Actor-Critic Methods - A2C and A3C
- The TextWorld Environment
- Web Navigation
- Continuous Action Space
- Trust Region Methods
- Black-Box Optimizations in RL
- Advanced Exploration
- Reinforcement Learning with Human Feedback
- AlphaGo Zero and MuZero
- RL in Discrete Optimization
- Multi-Agent RL
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