Adaptive Representations for Reinforcement Learning
Published on 5. October 2010
Book
Hardback
XIII, 116 pages
978-3-642-13931-4 (ISBN)
Description
This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own representations have the potential to dramatically improve performance. This book introduces two novel approaches for automatically discovering high-performing representations.
The first approach synthesizes temporal difference methods, the traditional approach to reinforcement learning, with evolutionary methods, which can learn representations for a broad class of
optimization problems. This synthesis is accomplished by customizing evolutionary
methods to the on-line nature of reinforcement learning and using them to evolve representations for value function approximators.
The second approach automatically learns representations based on piecewise-constant approximations of value functions. It begins with coarse representations and gradually refines them during learning, analyzing the current policy and value function to deduce the best refinements.
This book also introduces a novel method for devising input representations. This method addresses the feature selection problem by extending an algorithm that evolves the topology and weights of neural networks such that it evolves their inputs too.
In addition to introducing these new methods, this book presents extensive empirical results in multiple domains demonstrating that these techniques can substantially improve performance over methods
with manual representations.
More details
Series
Edition
1st Edition.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
XIII, 116 p.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 13 mm
Weight
396 gr
ISBN-13
978-3-642-13931-4 (9783642139314)
DOI
10.1007/978-3-642-13932-1
Schweitzer Classification
Other editions
Additional editions
Shimon Whiteson
Adaptive Representations for Reinforcement Learning
Book
11/2014
Springer
€106.99
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Shimon Whiteson
Adaptive Representations for Reinforcement Learning
E-Book
07/2010
Springer
€96.29
Available for download
Person
Diederik M. Roijers completed his master's in Computing Science at Utrecht University before obtaining his Ph.D. in Artificial Intelligence under the supervision of Shimon Whiteson and Frans A. Oliehoek at the University of Amsterdam in 2016. He then joined the University of Oxford as a postdoctoral research assistant. He was awarded a Postdoctoral Fellowship Grant from the FWO (Research Foundation - Flanders) and started as an FWO Postdoctoral Fellow at the Vrije Universiteit Brussel in October 2016. His research focuses on creating intelligent autonomous systems that assist humans in solving complex problems, especially those with multiple objectives. To this end, he focuses ondecision-theoretic planning and learning, which enable agents to use mathematical models to reason about the environments in which they operate. In the multi-objective problems he has been studying, the agents produce a set of possibly optimal policies that offer different trade-offs with respect to the objectives, to help users make an informed decision.Shimon Whiteson studied English and Computer Science at Rice University before completing his doctorate in Computer Science under the supervision of Peter Stone at the University of Texas at Austin in 2007. He then spent eight years as an Assistant and then an Associate Professor at the University of Amsterdam before joining the University of Oxford as an Associate Professor in 2015. He was awarded an ERC Starting Grant from the European Research Council in 2014. His research focuses on artificial intelligence with the goal of designing, analyzing, and evaluating the algorithms that enable computational systems to acquire and execute intelligent behavior. He is particularly interested in machine learning, with which computers can learn from experience, and decision-theoretic planning, with which they can reason about their goals and deduce behavioral strategies that maximize their utility. In addition to theoretical work on these topics, he has in recent years also focused on applying them to practical problems in robotics and search engine optimization.
Content
Part 1 Introduction.- Part 2 Reinforcement Learning.- Part 3 On-Line Evolutionary Computation.- Part 4 Evolutionary Function Approximation.- Part 5 Sample-Efficient Evolutionary Function Approximation.- Part 6 Automatic Feature Selection for Reinforcement Learning.- Part 7 Adaptive Tile Coding.- Part 8 RelatedWork.- Part 9 Conclusion.- Part 10 Statistical Significance.