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 factorize large networked systems of many agents into multiple local regions such that we can build separate simulators that run independently and in parallel. To monitor the influence that the different local regions 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
Original language | English |
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Title of host publication | Advances in Neural Information Processing Systems |
Editors | S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho , A. Oh |
Publisher | Morgan Kaufmann Publishers |
Number of pages | 14 |
Edition | 36 |
ISBN (Electronic) | 9781713871088 |
Publication status | Published - 2022 |
Event | 36th Conference on Neural Information Processing Systems - Hybrid Conference, New Orleans, United States Duration: 28 Nov 2022 → 9 Dec 2022 Conference number: 36 |
Conference
Conference | 36th Conference on Neural Information Processing Systems |
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Abbreviated title | NeurIPS 2022 |
Country/Territory | United States |
City | New Orleans |
Period | 28/11/22 → 9/12/22 |