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This paper presents a data-driven approach for multi-robot coordination inpartially-observable domains based on Decentralized Partially Observable MarkovDecision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide ageneral framework for cooperative sequential decision making under uncertaintyand MAs allow temporally extended and asynchronous action execution. To date,most methods assume the underlying Dec-POMDP model is known a priori or a fullsimulator is available during planning time. Previous methods which aim toaddress these issues suffer from local optimality and sensitivity to initialconditions. Additionally, few hardware demonstrations involving a large team ofheterogeneous robots and with long planning horizons exist. This work addressesthese gaps by proposing an iterative sampling based Expectation-Maximizationalgorithm (iSEM) to learn polices using only trajectory data containingobservations, MAs, and rewards. Our experiments show the algorithm is able toachieve better solution quality than the state-of-the-art learning-basedmethods. We implement two variants of multi-robot Search and Rescue (SAR)domains (with and without obstacles) on hardware to demonstrate the learnedpolicies can effectively control a team of distributed robots to cooperate in apartially observable stochastic environment.

Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions