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Author Scherrer, B. ♦ Charpillet, F.
Sponsorship IEEE Comput. Soc. ♦ Inf. Technol. Res. Inst. ♦ Wright State Univ
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2002
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Iterative algorithms ♦ Organisms ♦ Ecosystems ♦ Artificial intelligence
Abstract Solving multiagent reinforcement learning problems is a key issue. Indeed, the complexity of deriving multiagent plans, especially when one uses an explicit model of the problem, is dramatically increasing with the number of agents. This papers introduces a general iterative heuristic: at each step one chooses a sub-group of agents and update their policies to optimize the task given the rest of agents have fixed plans. We analyse this process in a general purpose and show how it can be applied to Markov decision processes, partially observable Markov decision processes and decentralized partially observable Markov decision processes.
Description Author affiliation: LORIA-INRIA Lorraine, Vandoeuvre-les-Nancy, France (Scherrer, B.; Charpillet, F.)
ISBN 0769518494
ISSN 10823409
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2002-11-04
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 271.26 kB
Page Count 6
Starting Page 463
Ending Page 468

Source: IEEE Xplore Digital Library