Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments

AAAI, pp. 2523-2529, 2016.

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Abstract:

Decentralized partially observable Markov decision processes (Dec-POMDPs) provide a general framework for multiagent sequential decision-making under uncertainty. Although Dec-POMDPs are typically intractable to solve for real-world problems, recent research on macro-actions (i.e., temporally-extended actions) has significantly increased ...More

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