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Deep Reinforcement Learning in Python: A Hands-On Introduction

Deep Reinforcement Learning in Python: A Hands-On Introduction

Autorzy
Wydawnictwo Pearson Education (US)
Data wydania 02/12/2019
Liczba stron 360
Forma publikacji książka w miękkiej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
ISBN 9780135172384
Kategorie Bazy danych, Sztuczna inteligencja
232.00 PLN (z VAT)
$61.73 / €51.85 / £46.20 /
Produkt na zamówienie
Dostawa 14 dni
Ilość
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Opis książki

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

Deep Reinforcement Learning in Python: A Hands-On Introduction

Spis treści

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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