Recent Advances in Reinforcement Learning

Softcover Nachdruck of the original 1. Auflage 1996
ISBN: 978-1-4419-5160-1
Verlag: Springer US

Buch, Englisch, 292 Seiten, Previously published in hardcover, Format (B × H): 170 mm x 244 mm, Gewicht: 515 g

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.
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 (Volume 22, Numbers 1, 2 and 3).

Zielgruppe

Research

Autoren/Hrsg.

Kaelbling, Leslie Pack

Fachgebiete

• Naturwissenschaften Physik Angewandte Physik Statistische Physik, Dynamische Systeme
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik

Inhaltsverzeichnis

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.