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.

• Type: Conference
• Year: 2004
• Where: ATAL
• Authors: Michael Weinberg, Jeffrey S. Rosenschein
• Keywords: ATAL 2004, Learning Algorithm, Multiagent Learning, Multiagent Learning Algorithm