Monte-Carlo Expectation Maximization for Decentralized POMDPs
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 397-402.
Abstract
We address two significant drawbacks of state-of-the-art solvers of decentralized POMDPs (DEC-POMDPs): 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 naive implementation of MCEM is inadequate in multi-agent 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.
@inproceedings{ijcai2013p66,
title = {Monte-Carlo Expectation Maximization for Decentralized POMDPs},
author = {Wu, Feng and Zilberstein, Shlomo and Jennings, Nicholas R.},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {397--402},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/66},
}
TY - CPAPER TI - Monte-Carlo Expectation Maximization for Decentralized POMDPs AU - Wu, Feng AU - Zilberstein, Shlomo AU - Jennings, Nicholas R. T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 397 EP - 402 UR - https://ijcai.org/proceedings/2013/66 ER -Copied
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