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ATAL
2004
Springer

Best-Response Multiagent Learning in Non-Stationary Environments

13 years 9 months ago
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 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 opponent’s non-stationary strategy, and simultaneously creates a best-response policy against that strategy. Our learning algorithm works within the very general framework of Ò-player, general-sum stochastic games, and learns both the game structure and its associated optimal policy.
Michael Weinberg, Jeffrey S. Rosenschein
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ATAL
Authors Michael Weinberg, Jeffrey S. Rosenschein
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