Sampling-Based Coordination-Informed Multi-Objective Multi-Robot Reinforcement Learning

Abstract:Multi-robot systems must simultaneously optimize competing objectives while maintaining coordinated behavior. Existing multi-agent reinforcement learning approaches often rely on fixed or centralized coordination, which limits adaptability and violates distributed constraints. This work introduces the Coordination-Informed Multi-Objective Reinforcement Learning (CIMORL) framework, integrating a distributed weight prediction mechanism, a privileged expert training strategy, and theoretical guarantees for Pareto-optimal solutions. We present the base CIMORL method alongside two sampling-based variants, CIMORL-TS (Tree Search) and CIMORL-MPPI (MPPI), which leverage privileged global information during training to enable fully decentralized deployment. Experimental validation in cooperative and adversarial scenarios demonstrates a $21.2\%$ hypervolume improvement and superior policy stability compared to state-of-the-art baselines. Real-world experiments with Crazyflie drones further validate the framework's robustness in resource allocation and multi-attacker multi-defend scenarios under partial observability.

Robotics (cs.RO); Multiagent Systems (cs.MA)

arXiv:2606.30893 [cs.RO]

(or arXiv:2606.30893v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2606.30893 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TRO.2026.3723950 Focus to learn more DOI(s) linking to related resources

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