Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control
Abstract
Experimental advances enabling high-resolution external control create new opportunities to produce materials with exotic properties. In this work, we investigate how a multi-agent reinforcement learning approach can be used to design external control protocols for self-assembly. We find that a fully decentralized approach performs remarkably well even with a "coarse" level of external control. More importantly, we see that a partially decentralized approach, where we include information about the local environment allows us to better control our system towards some target distribution. We explain this by analyzing our approach as a partially-observed Markov decision process. With a partially decentralized approach, the agent is able to act more presciently, both by preventing the formation of undesirable structures and by better stabilizing target structures as compared to a fully decentralized approach.
Cite
@article{arxiv.2111.06875,
title = {Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control},
author = {Shriram Chennakesavalu and Grant M. Rotskoff},
journal= {arXiv preprint arXiv:2111.06875},
year = {2021}
}
Comments
To appear in the Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)