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Foundations of Deep Reinforcement Learning: Theory and Practice in Python

Laura Graesser

Wah Loon Keng

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Overview

Description

In just a few years, deep reinforcement learning (DRL) systems such as DeepMinds DQN have yielded remarkable results. This hybrid approach to machine learning shares many similarities with human learning: its unsupervised self-learning, self-discovery of strategies, usage of memory, balance of exploration and exploitation, and its exceptional flexibility. Exciting in its own right, DRL may presage even more remarkable advances in general artificial intelligence.

Deep Reinforcement Learning in Python: A Hands-On Introduction is the fastest and most accessible way to get started with DRL. The authors teach through practical hands-on examples presented with their advanced OpenAI Lab framework. While providing a solid theoretical overview, they emphasize building intuition for the theory, rather than a deep mathematical treatment of results. Coverage includes:

Components of an RL system, including environment and agents
Value-based algorithms: SARSA, Q-learning and extensions, offline learning
Policy-based algorithms: REINFORCE and extensions; comparisons with value-based techniques
Combined methods: Actor-Critic and extensions; scalability through async methods
Agent evaluation
Advanced and experimental techniques, and more

Reviews

“This book provides an accessible introduction to deep reinforcement learning covering the mathematical concepts behind popular algorithms as well as their practical implementation. I think the book will be a valuable resource for anyone looking to apply deep reinforcement learning in practice.”
–Volodymyr Mnih, lead developer of DQN

“An excellent book to quickly develop expertise in the theory, language, and practical implementation of deep reinforcement learning algorithms. A limpid exposition which uses familiar notation; all the most recent techniques explained with concise, readable code, and not a page wasted in irrelevant detours: it is the perfect way to develop a solid foundation on the topic.”
–Vincent Vanhoucke, principal scientist, Google

“As someone who spends their days trying to make deep reinforcement learning methods more useful for the general public, I can say that Laura and Keng’s book is a welcome addition to the literature. It provides both a readable introduction to the fundamental concepts in reinforcement learning as well as intuitive explanations and code for many of the major algorithms in the field. I imagine this will become an invaluable resource for individuals interested in learning about deep reinforcement learning for years to come.”
–Arthur Juliani, senior machine learning engineer, Unity Technologies

“Until now, the only way to get to grips with deep reinforcement learning was to slowly accumulate knowledge from dozens of different sources. Finally, we have a book bringing everything together in one place.”
–Matthew Rahtz, ML researcher, ETH Zürich

Features

How to achieve breakthrough machine learning performance by combining deep neural networks with reinforcement learning
Reduces the learning curve by relying on the authors’ OpenAI Lab framework: requires less upfront theory, math, and programming expertise
Provides well-designed, modularized, and tested code examples with complete experimental data sets to illuminate the underlying algorithms
Includes case studies, practical tips, definitions, and other aids to learning and mastery
Prepares readers for exciting future advances in artificial general intelligence

Foreword xix
Preface xxi
Acknowledgments xxv
About the Authors xxvii

Chapter 1: Introduction to Reinforcement Learning 1
1.1 Reinforcement Learning 1
1.2 Reinforcement Learning as MDP 6
1.3 Learnable Functions in Reinforcement Learning 9
1.4 Deep Reinforcement Learning Algorithms 11
1.5 Deep Learning for Reinforcement Learning 17
1.6 Reinforcement Learning and Supervised Learning 19
1.7 Summary 21

Part I: Policy-Based and Value-Based Algorithms 23

Chapter 2: REINFORCE 25
2.1 Policy 26
2.2 The Objective Function 26
2.3 The Policy Gradient 27
2.4 Monte Carlo Sampling 30
2.5 REINFORCE Algorithm 31
2.6 Implementing REINFORCE 33
2.7 Training a REINFORCE Agent 44
2.8 Experimental Results 47
2.9 Summary 51
2.10 Further Reading 51
2.11 History 51

Chapter 3: SARSA 53
3.1 The Q- and V-Functions 54
3.2 Temporal Difference Learning 56
3.3 Action Selection in SARSA 65
3.4 SARSA Algorithm 67
3.5 Implementing SARSA 69
3.6 Training a SARSA Agent 74
3.7 Experimental Results 76
3.8 Summary 78
3.9 Further Reading 79
3.10 History 79

Chapter 4: Deep Q-Networks (DQN) 81
4.1 Learning the Q-Function in DQN 82
4.2 Action Selection in DQN 83
4.3 Experience Replay 88
4.4 DQN Algorithm 89
4.5 Implementing DQN 91
4.6 Training a DQN Agent 96
4.7 Experimental Results 99
4.8 Summary 101
4.9 Further Reading 102
4.10 History 102

Chapter 5: Improving DQN 103
5.1 Target Networks 104
5.2 Double DQN 106
5.3 Prioritized Experience Replay (PER) 109
5.4 Modified DQN Implementation 112
5.5 Training a DQN Agent to Play Atari Games 123
5.6 Experimental Results 128
5.7 Summary 132
5.8 Further Reading 132

Part II: Combined Methods 133

Chapter 6: Advantage Actor-Critic (A2C) 135
6.1 The Actor 136
6.2 The Critic 136
6.3 A2C Algorithm 141
6.4 Implementing A2C 143
6.5 Network Architecture 148
6.6 Training an A2C Agent 150
6.7 Experimental Results 157
6.8 Summary 161
6.9 Further Reading 162
6.10 History 162

Chapter 7: Proximal Policy Optimization (PPO) 165
7.1 Surrogate Objective 165
7.2 Proximal Policy Optimization (PPO) 174
7.3 PPO Algorithm 177
7.4 Implementing PPO 179
7.5 Training a PPO Agent 182
7.6 Experimental Results 188
7.7 Summary 192
7.8 Further Reading 192

Chapter 8: Parallelization Methods 195
8.1 Synchronous Parallelization 196
8.2 Asynchronous Parallelization 197
8.3 Training an A3C Agent 200
8.4 Summary 203
8.5 Further Reading 204

Chapter 9: Algorithm Summary 205

Part III: Practical Details 207

Chapter 10: Getting Deep RL to Work 209
10.1 Software Engineering Practices 209
10.2 Debugging Tips 218
10.3 Atari Tricks 228
10.4 Deep RL Almanac 231
10.5 Summary 238

Chapter 11: SLM Lab 239
11.1 Algorithms Implemented in SLM Lab 239
11.2 Spec File 241
11.3 Running SLM Lab 246
11.4 Analyzing Experiment Results 247
11.5 Summary 249

Chapter 12: Network Architectures 251
12.1 Types of Neural Networks 251
12.2 Guidelines for Choosing a Network Family 256
12.3 The Net API 262
12.4 Summary 271
12.5 Further Reading 271

Chapter 13: Hardware 273
13.1 Computer 273
13.2 Data Types 278
13.3 Optimizing Data Types in RL 280
13.4 Choosing Hardware 285
13.5 Summary 285

Part IV: Environment Design 287

Chapter 14: States 289
14.1 Examples of States 289
14.2 State Completeness 296
14.3 State Complexity 297
14.4 State Information Loss 301
14.5 Preprocessing 306
14.6 Summary 313

Chapter 15: Actions 315
15.1 Examples of Actions 315
15.2 Action Completeness 318
15.3 Action Complexity 319
15.4 Summary 323
15.5 Further Reading: Action Design in Everyday Things 324

Chapter 16: Rewards 327
16.1 The Role of Rewards 327
16.2 Reward Design Guidelines 328
16.3 Summary 332

Chapter 17: Transition Function 333
17.1 Feasibility Checks 333
17.2 Reality Check 335
17.3 Summary 337

Epilogue 338

Appendix A: Deep Reinforcement Learning Timeline 343

Appendix B: Example Environments 345
B.1 Discrete Environments 346
B.2 Continuous Environments 350

References 353
Index 363

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Graesser & Keng
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About the Author(s)

Laura Graesser is a research software engineer working in robotics at Google. She holds a master’s degree in computer science from New York University, where she specialized in machine learning.

Wah Loon Keng is an AI engineer at Machine Zone, where he applies deep reinforcement learning to industrial problems. He has a background in both theoretical physics and computer science.