---
title: "Monte-Carlo Expectation Maximization for Decentralized POMDPs"
authors:
  - "Feng Wu"
  - "Shlomo Zilberstein"
  - "Nicholas R. Jennings"
doi: ""
url: "https://ijcai.org/proceedings/2013/66"
pdf: "https://ijcai.org/proceedings/2013/0066.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "397-402"
track: ""
keywords:
---

# Monte-Carlo Expectation Maximization for Decentralized POMDPs

Feng Wu, Shlomo Zilberstein, Nicholas R. Jennings

*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.