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 Reductionmetric properties of the data. Theoretical underpinnings of the method are presented together with computational algorithms and successful applications in various areas of machine learning. The goal of this chapter is to introduce persistent homology as a practical tool for dimensionality reduction to reinforcement learning researchers.

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RODE

Reward Function Design in Reinforcement LearningNevertheless, the mainstream of RL research in recent years has been preoccupied with the development and analysis of learning algorithms, treating the reward signal as given and not subject to change. As the learning algorithms have matured, it is now time to revisit the questions of reward functio

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A Survey on Constraining Policy Updates Using the KL Divergenceampled from an environment eliminates the problem of accumulating model errors that model-based methods suffer from. However, model-free methods are less sample efficient compared to their model-based counterparts and may yield unstable policy updates when the step size between successive policy upd

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Fisher Information Approximations in Policy Gradient Methodson algorithms. The update direction in NPG-based algorithms is found by preconditioning the usual gradient with the inverse of the Fisher information matrix (FIM). Estimation and approximation of the FIM and FIM-vector products (FVP) are therefore of crucial importance for enabling applications of t

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Information-Loss-Bounded Policy Optimization as transforming the constrained TRPO problem into an unconstrained one, either via turning the constraint into a penalty or via objective clipping. In this chapter, an alternative problem reformulation is studied, where the information loss is bounded using a novel transformation of the KullbackLei