Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems
This addresses the problem of slow simulation for large networked multi-agent systems, offering a scalable solution for researchers and practitioners in reinforcement learning, though it is incremental as it builds on existing simulation and decomposition methods.
The paper tackles the high computational cost of simulating large multi-agent systems by decomposing them into local components with separate, parallel simulators, each using learned models to monitor inter-component influences, enabling training of large systems in just a few hours and mitigating negative effects of simultaneous learning.
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.