Multi-agent Inverse Reinforcement Learning for Zero-sum Games

In this paper we introduce a Bayesian framework for solving a class of problems termed Multi-agent Inverse Reinforcement Learning (MIRL). Compared to the well-known Inverse Reinforcement Learning (IRL) problem, MIRL is formalized in the context of a stochastic game rather than a Markov decision process (MDP). Games bring two primary challenges: First, the concept of optimality, central to MDPs, loses its meaning and must be replaced with a more general solution concept, such as the Nash equilibrium. Second, the non-uniqueness of equilibria means that in MIRL, in addition to multiple reasonable solutions for a given inversion model, there may be multiple inversion models that are all equally sensible approaches to solving the problem. We establish a theoretical foundation for competitive two-agent MIRL problems and propose a Bayesian optimization algorithm to solve the problem. We focus on the case of two-person zero-sum stochastic games, developing a generative model for the likelihood of unknown rewards of agents given observed game play assuming that the two agents follow a minimax bipolicy. As a numerical illustration, we apply our method in the context of an abstract soccer game. For the soccer game, we investigate relationships between the extent of prior information and the quality of learned rewards. Results suggest that covariance structure is more important than mean value in reward priors.