Foundations of Deep Reinforcement Learning: Theory and Practice in Python, Rough Cuts

Description

• Copyright 2020
• Pages: 416
• Edition: 1st

• Rough Cuts
• ISBN-10: 0-13-517300-0
• ISBN-13: 978-0-13-517300-8

This is the Rough Cut version of the printed book.
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.



Sample Content

Table of Contents

Foreword
Preface
AcknowledgmentsAbout the Authors

Chapter 1: Introduction to Reinforcement Learning

Part I: Policy-Based and Value-Based Algorithms

Chapter 2: Policy Gradient
Chapter 3: State Action Reward State Action
Chapter 4: Deep Q-Networks
Chapter 5: Improving Deep Q-Networks

Part II: Combined Methods

Chapter 6: Advantage Actor-Critic
Chapter 7: Proximal Policy Optimization
Chapter 8: Parallelization Methods
Chapter 9: Algorithm Summary

Part III: Practical Tips

Chapter 10: Getting Reinforcement Learning to Work
Chapter 11: SLM Lab
Chapter 12: Network Architectures
Chapter 13: Hardward
Chapter 14: Environment Design

Epilogue

Appendix A: Deep Reinforcement Learning Timeline
Appendix B: Example Environments

References
Index



Updates

Submit Errata

