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Reinforcement learning algorithms = ...

Belousov, Boris.

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Reinforcement learning algorithms = analysis and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning algorithms/ edited by Boris Belousov ... [et al.].
其他題名:	analysis and applications /
其他作者:	Belousov, Boris.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	viii, 206 p. :ill. (some col.), digital ;24 cm.
內容註:	Prediction Error and Actor-Critic Hypotheses in the Brain -- Reviewing on-policy / oﬀ-policy critic learning in the context of Temporal Diﬀerences and Residual Learning -- Reward Function Design in Reinforcement Learning -- Exploration Methods In Sparse Reward Environments -- A Survey on Constraining Policy Updates Using the KL Divergence -- Fisher Information Approximations in Policy Gradient Methods -- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies -- Information-Loss-Bounded Policy Optimization -- Persistent Homology for Dimensionality Reduction -- Model-free Deep Reinforcement Learning - Algorithms and Applications -- Actor vs Critic -- Bring Color to Deep Q-Networks -- Distributed Methods for Reinforcement Learning -- Model-Based Reinforcement Learning -- Challenges of Model Predictive Control in a Black Box Environment -- Control as Inference?
Contained By:	Springer Nature eBook
標題:	Reinforcement learning. -
電子資源:	https://doi.org/10.1007/978-3-030-41188-6
ISBN:	9783030411886

Reinforcement learning algorithms = analysis and applications /
Reinforcement learning algorithmsanalysis and applications /[electronic resource] :edited by Boris Belousov ... [et al.]. - Cham :Springer International Publishing :2021. - viii, 206 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v.8831860-949X ;. - Studies in computational intelligence ;v.883..

Prediction Error and Actor-Critic Hypotheses in the Brain -- Reviewing on-policy / oﬀ-policy critic learning in the context of Temporal Diﬀerences and Residual Learning -- Reward Function Design in Reinforcement Learning -- Exploration Methods In Sparse Reward Environments -- A Survey on Constraining Policy Updates Using the KL Divergence -- Fisher Information Approximations in Policy Gradient Methods -- Benchmarking the Natural gradient in Policy Gradient Methods and Evolution Strategies -- Information-Loss-Bounded Policy Optimization -- Persistent Homology for Dimensionality Reduction -- Model-free Deep Reinforcement Learning - Algorithms and Applications -- Actor vs Critic -- Bring Color to Deep Q-Networks -- Distributed Methods for Reinforcement Learning -- Model-Based Reinforcement Learning -- Challenges of Model Predictive Control in a Black Box Environment -- Control as Inference?

This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universitat Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.

ISBN: 9783030411886

Standard No.: 10.1007/978-3-030-41188-6doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

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520 $a This book reviews research developments in diverse areas of reinforcement learning such as model-free actor-critic methods, model-based learning and control, information geometry of policy searches, reward design, and exploration in biology and the behavioral sciences. Special emphasis is placed on advanced ideas, algorithms, methods, and applications. The contributed papers gathered here grew out of a lecture course on reinforcement learning held by Prof. Jan Peters in the winter semester 2018/2019 at Technische Universitat Darmstadt. The book is intended for reinforcement learning students and researchers with a firm grasp of linear algebra, statistics, and optimization. Nevertheless, all key concepts are introduced in each chapter, making the content self-contained and accessible to a broader audience.
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