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

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 parallelised 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.

Table of Contents:
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

About the Author :
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 specialised 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.

Review :
“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