Monte-Carlo Expectation Maximization for Decentralized POMDPs

By Feng Wu, Shlomo Zilberstein, Nicholas R. Jennings. 2013

50 citations recorded by the source corpus.

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Citations in the corpus

Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability (2017, methodology): “Recent scalable methods use Expectation Maximization to learn finite state controller (FSC) policies (Wu et al., 2013; Liu et al., 2015; 2016).”
Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions (2017, unknown): “POMDPs [19], [20] and MacDec-POMDPs [12].”
Stick-Breaking Policy Learning in Dec-POMDPs (2015, unknown): “Except for MCEM, no previous Dec-POMDPs algorithms are able to solve such large problems.”
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, methodology): “In particular, Monte-Carlo sampling with policy iteration and the expectation-maximization algorithm, are proposed in [219] and [220], respectively.”
A Concise Introduction to Decentralized POMDPs (2016, methodology): “Examples of such problems are settings for traffic control [Wu et al., 2013], communication network control [Winstein and Balakrishnan, 2013] and demonstrations on real multirobot systems [Emery-Montemerlo et al.”
Structured Exploration via Hierarchical Variational Policy Networks (2018, background): “In the multi-agent setting, the RL problem has also been considered as an inference problem in e.g. (Liu et al., 2015; Wu et al., 2013; Liu et al., 2016).”
Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments (2016, methodology): “These constraints encode validity information extracted from trajectories that are given.”