Learning for Multi-robot Cooperation in Partially Observable Stochastic Environments with Macro-actions
IROS, pp. 1853-1860, 2017.
This paper presents a data-driven approach for multi-robot coordination in partially-observable domains based on Decentralized Partially Observable Markov Decision Processes (Dec-POMDPs) and macro-actions (MAs). Dec-POMDPs provide a general framework for cooperative sequential decision making under uncertainty and MAs allow temporally ext...More
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