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arXiv · 1911.12825

Option-Critic in Cooperative Multi-agent Systems

Jhelum Chakravorty·Nadeem Ward·Julien Roy·Maxime Chevalier-Boisvert·Sumana Basu·Andrei Lupu·Doina Precup
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Abstract

In this paper, we investigate learning temporal abstractions in cooperative multi-agent systems, using the options framework (Sutton et al, 1999). First, we address the planning problem for the decentralized POMDP represented by the multi-agent system, by introducing a \emph{common information approach}. We use the notion of \emph{common beliefs} and broadcasting to solve an equivalent centralized POMDP problem. Then, we propose the Distributed Option Critic (DOC) algorithm, which uses centralized option evaluation and decentralized intra-option improvement. We theoretically analyze the asymptotic convergence of DOC and build a new multi-agent environment to demonstrate its validity. Our experiments empirically show that DOC performs competitively against baselines and scales with the number of agents.

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Jhelum Chakravorty, Nadeem Ward, Julien Roy, Maxime Chevalier-Boisvert, Sumana Basu, Andrei Lupu, Doina Precup. 2019-11-28. Option-Critic in Cooperative Multi-agent Systems. https://arxiv.org/abs/1911.12825

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