ReCoDe improves handcrafted multi-agent controllers by learning a dynamic quadratic constraint that controls how tightly each robot follows a learned reference action, outperforming baselines across navigation and consensus tasks.
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ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination
ReCoDe improves handcrafted multi-agent controllers by learning a dynamic quadratic constraint that controls how tightly each robot follows a learned reference action, outperforming baselines across navigation and consensus tasks.