Robust Stochastic Bayesian Games for Behavior Space Coverage
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A key challenge in multi-agent systems is the design of intelligent agents solving real-world tasks in close interaction with other agents (e.g. humans), thereby being confronted with a variety of behavioral variations and limited knowledge about the true behaviors of observed agents. The practicability of existing works addressing this challenge is being limited due to using finite sets of hypothesis for behavior prediction, the lack of a hypothesis design process ensuring coverage over all behavioral variations and sample-inefficiency when modeling continuous behavioral variations. In this work, we present an approach to this challenge based on a new framework of Robust Stochastic Bayesian Games (RSBGs). An RSBG defines hypothesis sets by partitioning the physically feasible, continuous behavior space of the other agents. It combines the optimality criteria of the Robust Markov Decision Process (RMDP) and the Stochastic Bayesian Game (SBG) to exponentially reduce the sample complexity for planning with hypothesis sets defined over continuous behavior spaces. In an intersection crossing task with broad continuous behavioral variations, we find that our approach outperforms the state-of-the-art algorithms achieving the same performance as a planning algorithm with knowledge of the true behaviors of other agents.
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