Computer Science > Machine Learning
[Submitted on 1 Jul 2022 (v1), last revised 1 Mar 2024 (this version, v2)]
Title:Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
View PDF HTML (experimental)Abstract:Due to its high sample complexity, simulation is, as of today, critical for the successful application of reinforcement learning. Many real-world problems, however, exhibit overly complex dynamics, which makes their full-scale simulation computationally slow. In this paper, we show how to decompose large networked systems of many agents into multiple local components such that we can build separate simulators that run independently and in parallel. To monitor the influence that the different local components exert on one another, each of these simulators is equipped with a learned model that is periodically trained on real trajectories. Our empirical results reveal that distributing the simulation among different processes not only makes it possible to train large multi-agent systems in just a few hours but also helps mitigate the negative effects of simultaneous learning.
Submission history
From: Miguel Suau [view email][v1] Fri, 1 Jul 2022 09:33:33 UTC (915 KB)
[v2] Fri, 1 Mar 2024 08:36:33 UTC (922 KB)
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