Recent Advances in Reinforcement Learning
Author:   Leslie Pack Kaelbling
Publisher:   Springer
Edition:   Reprinted from MACHINE LEARNING 22:1-3, 1996
ISBN:  

9780792397052


Pages:   292
Publication Date:   31 March 1996
Format:   Hardback
Availability:   In Print   Availability explained
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Recent Advances in Reinforcement Learning


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Overview

This work addresses research in an area that is gaining popularity in the artificial intelligence and neural network communities. Reinforcement learning has become a primary paradigm of machine learning. It applies to problems in which an agent (such as a robot, a process controller, or an information-retrieval engine) has to learn how to behave given only information about the success of its current actions. This book is a collection of papers that address topics including the theoretical foundations of dynamic programming approaches, the role of prior knowledge, and methods for improving performance of reinforcement-learning techniques. These papers build on previous work and form a resource for students and researchers in the area.

Full Product Details

Author:   Leslie Pack Kaelbling
Publisher:   Springer
Imprint:   Springer
Edition:   Reprinted from MACHINE LEARNING 22:1-3, 1996
Dimensions:   Width: 15.50cm , Height: 1.70cm , Length: 23.50cm
Weight:   1.310kg
ISBN:  

9780792397052


ISBN 10:   0792397053
Pages:   292
Publication Date:   31 March 1996
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

Editorial.- Efficient Reinforcement Learning through Symbiotic Evolution.- Linear Least-Squares Algorithms for Temporal Difference Learning.- Feature-Based Methods for Large Scale Dynamic Programming.- On the Worst-Case Analysis of Temporal-Difference Learning Algorithms.- Reinforcement Learning with Replacing Eligibility Traces.- Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results.- The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks.- The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms.- Creating Advice-Taking Reinforcement Learners.- Technical Note.

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