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Decentralized Markov Decision Process (Dec-MDP) provides a rich framework to represent cooperative decentralized and stochastic planning problems under transition uncertainty. However, solving a Dec-MDP to generate coordinated yet decentralized policies is NEXP-Hard. Researchers have made significant progress in providing approximate approaches to improve scalability with respect to number of agents. However, there has been little or no research devoted to finding guarantees on solution qualitydoi:10.1609/aaai.v31i1.10709 fatcat:7kbtwbkmzvb3vgwx4fpxf6etja