Stick-Breaking Policy Learning in Dec-POMDPs

IJCAI, 2015.

Cited by: 24|Bibtex|Views8
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Abstract:

Expectation maximization (EM) has recently been shown to be an efficient algorithm for learning finite-state controllers (FSCs) in large decentralized POMDPs (Dec-POMDPs). However, current methods use fixed-size FSCs and often converge to maxima that are far from the optimal value. This paper represents the local policy of each agent usin...More

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