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Due to its high sample complexity, simulation is, as of today, critical forthe successful application of reinforcement learning. Many real-world problems,however, exhibit overly complex dynamics, which makes their full-scalesimulation computationally slow. In this paper, we show how to decompose largenetworked systems of many agents into multiple local components such that wecan build separate simulators that run independently and in parallel. Tomonitor the influence that the different local components exert on one another,each of these simulators is equipped with a learned model that is periodicallytrained on real trajectories. Our empirical results reveal that distributingthe simulation among different processes not only makes it possible to trainlarge multi-agent systems in just a few hours but also helps mitigate thenegative effects of simultaneous learning.

Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems