Foundations of Deep Reinforcement Learning: Theory and Practice in Python (Addison-Wesley Data & Analytics Series)
Authors: Laura Graesser – Wah Loon Keng
ISBN-10: 0135172381
ISBN-13: 9780135172384
Edition 版次: 1
Publication Date 出版日期: 2019-12-15
Print Length 页数: 416 pages
Book Description
By finelybook
The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice
Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning,in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems,from single and multiplayer games—such as Go,Atari games,and DotA 2—to robotics.
Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition,then carefully explains the theory of deep RL algorithms,discusses implementations in its companion software library SLM Lab,and finishes with the practical details of getting deep RL to work.
Understand each key aspect of a deep RL problem
Explore policy- and value-based algorithms,including REINFORCE,SARSA,DQN,Double DQN,and Prioritized Experience Replay (PER)
Delve into combined algorithms,including Actor-Critic and Proximal Policy Optimization (PPO)
Understand how algorithms can be parallelized synchronously and asynchronously
Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work
Explore algorithm benchmark results with tuned hyperparameters
Understand how deep RL environments are designed
This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.
Contents
Preface
Acknowledgements
About the Authors
Chapter 1. Introduction
1.1Reinforcement Learning
1.2Reinforcement Learning as MDP
1.3 Learnable Functions in Reinforcement Learning
1.4Deep Reinforcement Learning Algorithms
1.5Deep Learning for Reinforcement Learning
1.6Reinforcement Learning and Supervised Learning
1.7 Summary
Part l: Policy-based & Value-based Algorithms
Chapter 2. Reinforce
Chapter 3. SARSA
Chapter 4. Deep Q-Networks(DQN)
Chapter 5. Improving DQN
Part l: Combined methods
Chapter 6. Advantage Actor-Critic(A2C)
Chapter 7. Proximal Policy Optimization(PPO)
Chapter 8. Parallelization Methods
Chapter 9. Algorithm Summary
Part ll: Practical Tips
Chapter 10. Getting Deep RL to Work
Chapter 11. SLM Lab
Chapter 12. Network architectures
Chapter 13. Hardware
Chapter 14. Environment Design
Epilogue
Appendix A. Deep Reinforcement Learning Timeline
Appendix B. Example Environments
Bibliography
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