Cooperative multi-agent reinforcement learning for high-dimensional nonequilibrium control
This work addresses the challenge of high-dimensional nonequilibrium control in materials science, offering an incremental improvement in protocol design for experimental applications.
The paper tackled the problem of designing external control protocols for self-assembly in materials science using multi-agent reinforcement learning, finding that a partially decentralized approach outperforms a fully decentralized one by better controlling the system towards target distributions and stabilizing structures.
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.