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Reinforcement Learning Algorithms: Analysis and Applications

BuchKartoniert, Paperback
206 Seiten
Englisch
Springererschienen am04.01.20221st ed. 2021
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.mehr
Verfügbare Formate
BuchGebunden
EUR149,79
BuchKartoniert, Paperback
EUR149,79

Produkt

KlappentextThis 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.
Zusammenfassung
Provides recent research on reinforcement learning algorithms

Presents the analysis and application alike

Written by respected experts in the field
Details
ISBN/GTIN978-3-030-41190-9
ProduktartBuch
EinbandartKartoniert, Paperback
Verlag
Erscheinungsjahr2022
Erscheinungsdatum04.01.2022
Auflage1st ed. 2021
ReiheStudies in Computational Intelligence
Seiten206 Seiten
SpracheEnglisch
IllustrationenVIII, 206 p. 45 illus., 35 illus. in color.
Artikel-Nr.50371008

Inhalt/Kritik

Inhaltsverzeichnis
Prediction Error and Actor-Critic Hypotheses in the Brain.-  Reviewing on-policy / oï¬-policy critic learning in the context of Temporal Diï¬erences 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?mehr

Autor

Boris Belousov is a Ph.D. student at Technische Universität Darmstadt, Germany, advised by Prof. Jan Peters. He received his M.Sc. degree from the University of Erlangen-Nuremberg, Germany, in 2016, supported by a DAAD scholarship for academic excellence. Boris is now working toward combining optimal control and information theory with applications to robotics and reinforcement learning.

Hany Abdulsamad is a Ph.D. student at the TU Darmstadt, Germany. He graduated with a Master's degree in Automation and Control from the faculty of Electrical Engineering and Information Technology at the TU Darmstadt. His research interests range from optimal control and trajectory optimization to reinforcement learning and robotics. Hany's current research focuses on learning hierarchical structures for system identification and control.
After graduating with a Master's degree in Autonomous Systems from the Technische Universität Darmstadt, Pascal Klink pursued his Ph.D. studies at the Intelligent Autonomous Systems Group of the TU Darmstadt, where he developed methods for reinforcement learning in unstructured, partially observable real-world environments. Currently, he is investigating curriculum learning methods and how to use them to facilitate learning in these environments.
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Herausgegeben:Belousov, Boris