Reinforcement Learning Algorithms: Analysis and Applications / edited by Boris Belousov, Hany Abdulsamad, Pascal Klink, Simone Parisi, Jan Peters.

Format:

Book

Contributor:

Belousov, Boris, editor.

Series:

Studies in Computational Intelligence, 1860-9503 ; 883

Language:

English

Subjects (All):

Computational intelligence.

Artificial intelligence.

Computational Intelligence.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Artificial Intelligence.

Physical Description:

1 online resource (VIII, 206 p. 45 illus., 35 illus. in color.)

Edition:

1st ed. 2021.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

Summary:

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 Universität 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.

Contents:

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?

ISBN:

9783030411886

3030411885