Titlebook: Reinforcement Learning Algorithms: Analysis and Applications; Boris Belousov,Hany Abdulsamad,Jan Peters Book 2021 The Editor(s) (if applic

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Persistent Homology for Dimensionality Reductionhine learning in general and in reinforcement learning in particular. This chapter serves as an introduction and overview of .—a powerful tool for dimensionality reduction from the field of topological data analysis. Among other approaches, persistent homology explicitly tries to capture salient geo

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Model-Free Deep Reinforcement Learning—Algorithms and Applicationscy and off-policy algorithms in the value-based and policy-based domain. Influences and possible drawbacks of different algorithmic approaches are analyzed and associated with new improvements in order to overcome previous problems. Further, the survey shows application scenarios for difficult domai

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Model-Based Reinforcement Learning from PILCO to PETS wider application of reinforcement learning. A popular algorithm called PILCO delivers on this promise by combining Gaussian process regression with policy search. However, PILCO comes at high computational costs and faces limitations in high-dimensional state-action spaces. A—at the time of writin

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Model-Based Reinforcement Learning from PILCO to PETSy establishing connections between those—at first glance—very different algorithms. For this, we introduce a common definition of the problem which model-based reinforcement learning algorithms try to solve and then investigate follow up work on PILCO.