Foundations of Deep Reinforcement Learning:Theory and Practice in Python (Addison-Wesley Data & Analytics Series)

Authors:Laura Graesser – Wah Loon Keng

ISBN-10 书号：0135172381

ISBN-13 书号：9780135172384

Edition 版次：1

Release Finelybook 出版日期：2019-12-15

pages 页数：416 pages

**Book Description**

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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