  The focus of this paper is 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 stochastic games, which generalize Markov decision processes
to game theoretic scenarios. We establish a theoretical foundation for
competitive two-agent zero-sum MIRL problems and propose a Bayesian solution
approach in which the generative model is based on an assumption that the two
agents follow a minimax bi-policy. Numerical results are presented comparing
the Bayesian MIRL method with two existing methods in the context of an
abstract soccer game. Investigation centers on 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.
