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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Summary: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 sense of finding a best-response policy, rather than in reaching an equilibrium. We present the first learning algorithm that is provably optimal against restricted classes of non-stationary opponents. The algorithm infers an accurate model of the opponentýs non-stationary strategy, and simultaneously creates a best-response policy against that strategy. Our learning algorithm works within the very general framework of n-player, general-sum stochastic games, and learns both the game structure and its associated optimal policy.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:9781581138641
1581138644
DOI:10.5555/1018410.1018798