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Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
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| 자료유형 | 단행본 |
|---|---|
| 서명/저자사항 | Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more/ Maxim Lapan. |
| 개인저자 | Lapan, Maxim,author. |
| 발행사항 | Birmingham, UK: Packt Publishing, 2018. |
| 형태사항 | 1 online resource (1 volume): illustrations. |
| 기타형태 저록 | Print version: Lapan, Maxim Deep Reinforcement Learning Hands-On : Apply Modern RL Methods, with Deep Q-Networks, Value Iteration, Policy Gradients, TRPO, AlphaGo Zero and More Birmingham : Packt Publishing Ltd,c2018 9781788834247 |
| ISBN | 9781788839303 1788839307 |
| 일반주기 |
"Expert insight."
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| 서지주기 | Includes bibliographical references and index. |
| 내용주기 | Table of ContentsWhat is Reinforcement Learning?OpenAI GymDeep Learning with PyTorchThe Cross-Entropy MethodTabular Learning and the Bellman EquationDeep Q-NetworksDQN ExtensionsStocks Trading Using RLPolicy Gradients - An AlternativeThe Actor-Critic MethodAsynchronous Advantage Actor-CriticChatbots Training with RL Web NavigationContinuous Action SpaceTrust Regions - TRPO, PPO, and ACKTRBlack-Box Optimization in RLBeyond Model-Free - ImaginationAlphaGo Zero. |
| 요약 | This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging problems than ever before. Apply deep RL methods to training your agent to beat arcade ... |
| 일반주제명 | Reinforcement learning. Machine learning. Natural language processing (Computer science) Artificial intelligence. Artificial intelligence. Machine learning. Natural language processing (Computer science) Reinforcement learning. COMPUTERS / General. |
| 언어 | 영어 |
| 바로가기 |  |
