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Decentralized POMDPs provide an expressive framework for multi-agentsequential decision making. While fnite-horizon DECPOMDPs have enjoyedsignifcant success, progress remains slow for the infnite-horizon case mainlydue to the inherent complexity of optimizing stochastic controllersrepresenting agent policies. We present a promising new class of algorithms forthe infnite-horizon case, which recasts the optimization problem as inferencein a mixture of DBNs. An attractive feature of this approach is thestraightforward adoption of existing inference techniques in DBNs for solvingDEC-POMDPs and supporting richer representations such as factored or continuousstates and actions. We also derive the Expectation Maximization (EM) algorithmto optimize the joint policy represented as DBNs. Experiments on benchmarkdomains show that EM compares favorably against the state-of-the-art solvers.

Anytime Planning for Decentralized POMDPs using Expectation Maximization