Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
arXiv:2606.24010v1 Announce Type: new Abstract: Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints. Existing approaches face a fundamental trade-off: learning-based methods achieve strong empirical performance but lack theoretical safety guarantees, while control-theoretic methods enforce safety but often lead to overly conservative and inefficient behaviors. We propose a hierarchical multi-agent reinforcement learning framework that enforces hard safety constraints under mild assumptions at low level via a constraint manifold, while enabling effective coordination through high-level policy learning. Our approach provides theoretical safety guarantees in the multi-agent setting and yields stationary learning dynamics, thereby enabling stable and efficient training. Empirically, our method achieves competitive performance while maintaining nearly perfect safety rates, and generalizes effectively to varying numbers of agents and obstacles.
[2606.24010] Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
[Submitted on 22 Jun 2026]
Title:Safe and Generalizable Hierarchical Multi-Agent RL via Constraint Manifold Control
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Abstract:Multi-agent systems are widely used in safety-critical applications that require coordinated behavior under strict safety constraints. Existing approaches face a fundamental trade-off: learning-based methods achieve strong empirical performance but lack theoretical safety guarantees, while control-theoretic methods enforce safety but often lead to overly conservative and inefficient behaviors. We propose a hierarchical multi-agent reinforcement learning framework that enforces hard safety constraints under mild assumptions at low level via a constraint manifold, while enabling effective coordination through high-level policy learning. Our approach provides theoretical safety guarantees in the multi-agent setting and yields stationary learning dynamics, thereby enabling stable and efficient training. Empirically, our method achieves competitive performance while maintaining nearly perfect safety rates, and generalizes effectively to varying numbers of agents and obstacles.
Comments: 10 pages
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.24010 [cs.AI]
(or arXiv:2606.24010v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.24010
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zihao Guo [view email] [v1] Mon, 22 Jun 2026 23:32:23 UTC (4,947 KB)
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