Foundations of Deep Reinforcement Learning Theory and Practice in Python
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
1 online resource (515 p.).
9780135172483, 0135172489
1338835718
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
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