CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning

Tianmeng Hu; Biao Luo; Ke Li

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arXiv · 2610.23820

CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning

Tianmeng Hu·Biao Luo·Ke Li

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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.

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BibTeX RIS

Tianmeng Hu, Biao Luo, Ke Li. 2026-10-16. CocoRNA: Collective RNA Design with Cooperative Multi-agent Reinforcement Learning. https://arxiv.org/abs/2610.23820

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