Monte-Carlo expectation maximization for decentralized POMDPs
OA Location
Author(s)
Wu, Feng
Zilberstein, S
Jennings, NR
Type
Conference 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.
Date Issued
2013
Citation
2013, pp.397-403
Publisher
Association for the Advancement of Artificial Intelligence
Start Page
397
End Page
403
Identifier
http://eprints.soton.ac.uk/351021/
Source
Proceedings of the 23rd International Joint Conference on AI (IJCAI)
Publication Status
Unpublished