{"type":"ScholarlyArticle","title":"Monte-Carlo Expectation Maximization for Decentralized POMDPs","authors":[{"name":"Feng Wu","slug":"feng-wu","given":"Feng","family":"Wu","affiliation":""},{"name":"Shlomo Zilberstein","slug":"shlomo-zilberstein","given":"Shlomo","family":"Zilberstein","affiliation":""},{"name":"Nicholas R. Jennings","slug":"nicholas-r-jennings","given":"Nicholas R.","family":"Jennings","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/66","pdf":"https://ijcai.org/proceedings/2013/0066.pdf","markdown":"https://ijcai.org/proceedings/2013/66.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/66","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":66,"pages":{"first":397,"last":402},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}