Recent advances in reinforcement learning

著者

書誌事項

Recent advances in reinforcement learning

edited by Leslie Pack Kaelbling

Kluwer Academic, c1996

  • : pbk

大学図書館所蔵 件 / 全20件

この図書・雑誌をさがす

注記

Reprinted from Machine Learning an International Journal vol. 22, Nos. 1, 2 & 3, January/February/March, 1996

Includes bibliographical references

内容説明・目次

内容説明

Recent Advances in Reinforcement Learning addresses current research in an exciting area that is gaining a great deal of 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 important 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 will form an important resource for students and researchers in the area. Recent Advances in Reinforcement Learning is an edited volume of peer-reviewed original research comprising twelve invited contributions by leading researchers. This research work has also been published as a special issue of Machine Learning (Volume 22, Numbers 1, 2 and 3).

目次

  • Editorial
  • T.G. Dietterich. Introduction
  • L.P. Kaelbling. Efficient Reinforcement Learning Through Symbiotic Evolution
  • D.E. Moriarty, R. Mikkulainen. Linear Least-Squares Algorithms for Temporal Difference Learning
  • S.J. Bradtke, A.G. Barto. Feature-Based Methods for Large Scale Dynamic Programming
  • J.N. Tsitsiklis, B. Van Roy. On the Worst-Case Analysis of Temporal-Difference Learning Algorithms
  • R.E. Schapire, M.K. Warmuth. Reinforcement Learning with Replacing Eligibility Traces
  • S.P. Singh, R.S. Sutton. Average Reward Reinforcement Learning: Foundations, Algorithms, and Empirical Results
  • S. Mahadevan. The Loss from Imperfect Value Functions in Expectation-Based and Minimax-Based Tasks
  • M. Heger. The Effect of Representation and Knowledge on Goal-Directed Exploration with Reinforcement-Learning Algorithms
  • S. Koenig, R.G. Simmons. Creating Advice-Taking Reinforcement Learners
  • R. Maclin, J.W. Shavlik. Technical Note: Incremental Multi-Step Q-Learning
  • J. Peng, R.J. Williams.

「Nielsen BookData」 より

詳細情報

ページトップへ