Individual Planning in Infinite-Horizon Multiagent Settings: Inference, Structure and Scalability

摘要

This paper provides the first formalization of self-interested planning in multiagent settings using expectation-maximization (EM). Our formalization in the context of infinite-horizon and finitely-nested interactive POMDPs (I-POMDP) is distinct from EM formulations for POMDPs and cooperative multiagent planning frameworks. We exploit the graphical model structure specific to l-POMDPs, and present a new approach based on block-coordinate descent for further speed up. Forward filtering-backward sampling - a combination of exact filtering with sampling - is explored to exploit problem structure.