{"ID":5439505,"CreatedAt":"2026-07-01T01:17:58.482524686Z","UpdatedAt":"2026-07-02T20:26:55.806500664Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30893","arxiv_id":"2606.30893","title":"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.","short_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-Info...","url_abs":"https://arxiv.org/abs/2606.30893","url_pdf":"https://arxiv.org/pdf/2606.30893v1","authors":"[\"Antonio Marino\",\"Esteban Restrepo\",\"Soon-jo Chung\",\"Paolo Robuffo Giordano\",\"Claudio Pacchierotti\"]","published":"2026-06-29T20:31:03Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.MA\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}