Best-Response Multiagent Learning in Non-Stationary Environments

This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learning algorithm and its evaluation criteria. In contrast, we propose a multiagent learning algorithm that is optimal in the...

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Bibliographic Details
Published in:	Autonomous Agents and Multiagent Systems: Proceedings, 3rd International Joint Conference, New York City, New York, 2004. pp. 506 - 513
Main Authors:	Weinberg, Michael, Rosenschein, Jeffrey S.
Format:	Conference Proceeding
Language:	English
Published:	Washington, DC, USA IEEE Computer Society 19.07.2004 IEEE
Series:	ACM Conferences
Subjects:	Algorithm design and analysis Autonomous agents Computer science Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems Computing methodologies > Artificial intelligence > Planning and scheduling Computing methodologies > Machine learning > Learning paradigms Computing methodologies > Machine learning > Machine learning approaches > Markov decision processes Convergence Game theory Learning Permission Process control Stochastic processes Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
ISBN:	9781581138641, 1581138644
Online Access:	Get full text
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