ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordination

Abstract

A hybrid, decentralised framework combining optimisation-based control with adaptation provided by MARL. Rather than discarding expert controllers, ReCoDe improves them by learning additional dynamic constraints that capture subtler behaviours — for example, constraining agent movements to prevent congestion in cluttered scenarios.

Paper