{"ID":2868736,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.15799","arxiv_id":"2509.15799","title":"Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control","abstract":"Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from poor sample efficiency and limited reliability, while model-based methods depend on predefined references and struggle to generalize. We propose a hierarchical framework that combines tactical decision-making via reinforcement learning (RL) with low-level execution through Model Predictive Control (MPC). For the case of multi-agent systems this means that high-level policies select abstract targets from structured regions of interest (ROIs), while MPC ensures dynamically feasible and safe motion. Tested on a predator-prey benchmark, our approach outperforms end-to-end and shielding-based RL baselines in terms of reward, safety, and consistency, underscoring the benefits of combining structured learning with model-based control.","short_abstract":"Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from poor sample efficiency and limited reliability, while model-based methods depend on predefined references and struggle to generalize. We prop...","url_abs":"https://arxiv.org/abs/2509.15799","url_pdf":"https://arxiv.org/pdf/2509.15799v2","authors":"[\"Max Studt\",\"Georg Schildbach\"]","published":"2025-09-19T09:27:15Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.AI\",\"cs.RO\",\"math.OC\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}