Title: CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Multi-agent, reinforcement learning, RNA inverse design

Abstract: Designing RNA sequences that reliably fold into specific secondary structures is essential for understanding their biological functions but remains a challenging computational problem. We propose CocoRNA, a cooperative multi-agent reinforcement learning framework for RNA inverse design. CocoRNA simplifies the design task by decomposing it into smaller sub-problems, each solved collaboratively by multiple agents. This approach reduces the complexity of the problem and improves the exploration of design policies. During training, a centralized critic uses global structural information to guide the agents, enabling them to jointly optimize their design strategies. As a result, CocoRNA learns high-quality RNA design policies that generalize effectively to unseen structures without additional training. Experiments on the Rfam dataset demonstrate that CocoRNA substantially outperforms state-of-the-art methods in both success rate and design speed. Further experiments on other biological sequence design tasks highlight the effectiveness and broad potential of CocoRNA for complex design tasks. Visualization examples are available on https://sites.google.com/view/cocorna/home.

TL;DR: We introduce CocoRNA, the first MARL framework for RNA design, which decomposes the task into cooperative sub-tasks and outperforms state-of-the-art methods in accuracy and efficiency.