Reinforcement Learning Algorithms

Analysis and Applications

By: Boris Belousov (Editor), Simone Parisi (Editor), Pascal Klink (Editor), Jan Peters (Editor), Hany Abdulsamad (Editor)

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Hardcover | 3 January 2021

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Hardcover
216 Pages

Dimensions(cm)
23.5 x 15.5 x 1.42

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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?

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