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Recent advances in Bayesian reinforcement learning (BRL) have shown thatBayes-optimality is theoretically achievable by modeling the environment'slatent dynamics using Flat-Dirichlet-Multinomial (FDM) prior. Inself-interested multi-agent environments, the transition dynamics are mainlycontrolled by the other agent's stochastic behavior for which FDM'sindependence and modeling assumptions do not hold. As a result, FDM does notallow the other agent's behavior to be generalized across different states norspecified using prior domain knowledge. To overcome these practical limitationsof FDM, we propose a generalization of BRL to integrate the general class ofparametric models and model priors, thus allowing practitioners' domainknowledge to be exploited to produce a fine-grained and compact representationof the other agent's behavior. Empirical evaluation shows that our approachoutperforms existing multi-agent reinforcement learning algorithms.

A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior