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).
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