Recent Advances in Reinforcement Learning -

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Résumé :
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).

Sommaire:
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.