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Reinforcement learningtheory and python implementation /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learningby Zhiqing Xiao.
其他題名:	theory and python implementation /
作者:	Xiao, Zhiqing.
出版者:	Singapore :Springer Nature Singapore :2024.
面頁冊數:	xxii, 559 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning.
電子資源:	https://doi.org/10.1007/978-981-19-4933-3
ISBN:	9789811949333$q(electronic bk.)

Reinforcement learningtheory and python implementation /
Xiao, Zhiqing.

Reinforcement learningtheory and python implementation /[electronic resource] :by Zhiqing Xiao. - Singapore :Springer Nature Singapore :2024. - xxii, 559 p. :ill., digital ;24 cm.

Chapter 1. Introduction of Reinforcement Learning (RL) -- Chapter 2. MDP: Markov Decision Process -- Chapter 3. Model-based Numerical Iteration -- Chapter 4. MC: Monte Carlo Learning -- Chapter 5. TD: Temporal Difference Learning -- Chapter 6. Function Approximation -- Chapter 7. PG: Policy Gradient -- Chapter 8. AC: Actor-Critic -- Chapter 9. DPG: Deterministic Policy Gradient -- Chapter 10. Maximum-Entropy RL -- Chapter 11. Policy-based Gradient-Free Algorithms -- Chapter 12. Distributional RL -- Chapter 13. Minimize Regret -- Chapter 14. Tree Search -- Chapter 15. More Agent-Environment Interfaces -- Chapter 16. Learn from Feedback and Imitation Learning.

Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux. This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.

ISBN: 9789811949333$q(electronic bk.)

Standard No.: 10.1007/978-981-19-4933-3doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

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505 0 $a Chapter 1. Introduction of Reinforcement Learning (RL) -- Chapter 2. MDP: Markov Decision Process -- Chapter 3. Model-based Numerical Iteration -- Chapter 4. MC: Monte Carlo Learning -- Chapter 5. TD: Temporal Difference Learning -- Chapter 6. Function Approximation -- Chapter 7. PG: Policy Gradient -- Chapter 8. AC: Actor-Critic -- Chapter 9. DPG: Deterministic Policy Gradient -- Chapter 10. Maximum-Entropy RL -- Chapter 11. Policy-based Gradient-Free Algorithms -- Chapter 12. Distributional RL -- Chapter 13. Minimize Regret -- Chapter 14. Tree Search -- Chapter 15. More Agent-Environment Interfaces -- Chapter 16. Learn from Feedback and Imitation Learning.
520 $a Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux. This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.
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