Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908584846032896 |
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| author | Studt, Max Schildbach, Georg |
| author_facet | Studt, Max Schildbach, Georg |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15799 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control Studt, Max Schildbach, Georg Systems and Control Artificial Intelligence Robotics Optimization and Control 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. |
| title | Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control |
| topic | Systems and Control Artificial Intelligence Robotics Optimization and Control |
| url | https://arxiv.org/abs/2509.15799 |