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Forward and Backward Bellman equations improve the efficiency of EM algorithm for DEC-POMDP

By Takehiro Tottori and Tetsuya Kobayashi
Decentralized partially observable Markov decision process (DEC-POMDP) models sequential decision making problems by a team of agents. Since the planning of DEC-POMDP can be interpreted as the maximum likelihood estimation for the latent variable model, DEC-POMDP can be solved by the EM algorithm. However, in EM for DEC-POMDP, the forward--backward... Show more
May 6, 2021
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Forward and Backward Bellman equations improve the efficiency of EM algorithm for DEC-POMDP
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