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Decentralized Partially-Observable Markov Decision Processes (Dec-POMDPs) are powerful theoretical models for deriving optimal coordination policies of agent teams in environments with uncertainty. Unfortunately, their general NEXP solution complexity [3] presents significant challenges when applying them to real-world problems, particularly those involving teams of more than two agents. Inevitably, the policy space becomes intractably large as agents coordinate joint decisions that are based on dissimilar beliefs about an uncertain world state and that involve performing actions with stochastic effects. Our work directly confronts the policy space explosion with the intuition that instead of coordinating all policy decisions, agents need only coordinate abstractions of their policies that constitute the essential influences that they exert on each other.

From policies to influences: a framework for nonlocal abstraction in transition-dependent Dec-POMDP agents