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Emerging coordination in infinite team Markov games

In this paper we address the problem of coordination in multi-agent sequential decision problems with infinite statespaces. We adopt a game theoretic formalism to describe the interaction of the multiple decision-makers and propose the novel approximate biased adaptive play algorithm. This algorithm is an extension of biased adaptive play to team Markov games defined over infinite state-spaces. We establish our method to coordinate with probability 1 in the optimal strategy and discuss how this methodology can be combined with approximate learning architectures. We conclude with two simple examples of application of our algorithm.