Best-response multiagent learning in non-stationary environments

This paper investigates a relatively new direction in Mul-tiagent 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 al-gorithm that is provably optimal against restricted classes of non-stationary opponents. The algorithm infers an accu-rate 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 gen-eral framework of n-player, general-sum stochastic games, and learns both the game structure and its associated opti-mal policy. 1