Encouragement of Right Social Norms by Inverse Reinforcement Learning

Abstract

This study is intended to encourage appropriate social norms among multiple agents. Effective norms, such as those emerging from sustained individual interactions over time, can make agents act cooperatively to optimize their performance. We introduce a “social learning” model in which agents mutually interact under a framework of the coordination game. Because coordination games have dual equilibria, social norms are necessary to make agents converge to a unique equilibrium. As described in this paper, we present the emergence of a right social norm by inverse reinforcement learning, which is an approach for extracting a reward function from the observation of optimal behaviors. First, we let a mediator agent estimate the reward function by inverse reinforcement learning from the observation of a master's behavior. Secondly, we introduce agents who act according to an estimated reward function in the multiagent world in which most agents, called citizens, have no way to act. Finally, we evaluate the effectiveness of introducing inverse reinforcement learning.