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Best-Response Multiagent Learning in Non-Stationary Environments

AAMAS, pp.506-513, (2004)

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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...更多

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