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Monte-Carlo expectation maximization for decentralized POMDPs

Monte-Carlo expectation maximization for decentralized POMDPs
Monte-Carlo expectation maximization for decentralized POMDPs
We address two significant drawbacks of state-of-the-art solvers of decentralized POMDPs (DECPOMDPs): the reliance on complete knowledge of the model and limited scalability as the complexity of the domain grows. We extend a recently proposed approach for solving DEC-POMDPs via a reduction to the maximum likelihood problem, which in turn can be solved using EM. We introduce a model-free version of this approach that employs Monte-Carlo EM (MCEM). While a naïve implementation of MCEM is inadequate in multiagent settings, we introduce several improvements in sampling that produce high-quality results on a variety of DEC-POMDP benchmarks, including large problems with thousands of agents.
978-1-57735-633-2
397-403
Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Zilberstein, S
f4822d34-5343-490c-a9ed-7be38f6c06d8
Jennings, N.R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30
Wu, Feng
b79f9800-2819-40c8-96e7-3ad85f866f5e
Zilberstein, S
f4822d34-5343-490c-a9ed-7be38f6c06d8
Jennings, N.R.
ab3d94cc-247c-4545-9d1e-65873d6cdb30

Wu, Feng, Zilberstein, S and Jennings, N.R. (2013) Monte-Carlo expectation maximization for decentralized POMDPs. Proceedings of the 23rd International Joint Conference on AI (IJCAI), China. 03 - 09 Aug 2013. pp. 397-403 .

Record type: Conference or Workshop Item (Paper)

Abstract

We address two significant drawbacks of state-of-the-art solvers of decentralized POMDPs (DECPOMDPs): the reliance on complete knowledge of the model and limited scalability as the complexity of the domain grows. We extend a recently proposed approach for solving DEC-POMDPs via a reduction to the maximum likelihood problem, which in turn can be solved using EM. We introduce a model-free version of this approach that employs Monte-Carlo EM (MCEM). While a naïve implementation of MCEM is inadequate in multiagent settings, we introduce several improvements in sampling that produce high-quality results on a variety of DEC-POMDP benchmarks, including large problems with thousands of agents.

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Published date: 2013
Venue - Dates: Proceedings of the 23rd International Joint Conference on AI (IJCAI), China, 2013-08-03 - 2013-08-09
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 351021
URI: http://eprints.soton.ac.uk/id/eprint/351021
ISBN: 978-1-57735-633-2
PURE UUID: eda73ca3-f14c-4c55-bbb0-bd371901d1e2

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Date deposited: 12 Apr 2013 15:41
Last modified: 28 Oct 2019 21:08

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Contributors

Author: Feng Wu
Author: S Zilberstein
Author: N.R. Jennings

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