2026-06-18原文2 min readUpdated: 2026-06-18

TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

TRIDENT, a novel MARL framework, addresses the three-way coupling of hybrid discrete-continuous actions, safety constraints, and physics dynamics. It introduces Richardson-Romberg gradient correction, Lyapunov-constrained trust-region updates, and a physics-informed residual critic, achieving an O~(1/√K) convergence rate and reducing training violations by up to 95.5% over baselines.

SourcearXiv Machine LearningAuthor: Zijie Meng, Ziwei Li, Yufei Liu, Zhiyu Li, Jiyuan Liu, Wenhua Nie, Bingcai Wei, Miao Zhang

[2606.18308] TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

[Submitted on 16 Jun 2026]

Title:TRIDENT: Breaking the Hybrid-Safety-Physics Coupling for Provably Safe Multi-Agent Reinforcement Learning

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Abstract:Safe coordination in networked cyber-physical systems forces learning algorithms to simultaneously handle hybrid discrete-continuous actions, hard training-time safety constraints, and physics-governed dynamics. We show that these three features form a directed cycle of biases that defeats any naive composition of off-the-shelf modules, and formalize this as a three-way coupling lemma. We then introduce TRIDENT, the first MARL framework whose three components are co-designed to cancel each leak: a Richardson-Romberg gradient correction reducing Gumbel-Softmax bias from O(tau) to O(tau^2), a Lyapunov-constrained sequential trust-region update enforcing per-iterate feasibility, and a physics-informed residual critic that decomposes value rather than reward. We prove an O~(1/sqrt(K)) convergence rate to a constrained Nash equilibrium and an O(sqrt(K)) cumulative-violation bound. On multi-UAV mobile-edge computing, autonomous intersection management, and a hybrid SMAC variant, TRIDENT cuts training-time violations by 95.5% over MADDPG and 76.3% over MACPO, while improving reward by 13.5% over the strongest unconstrained baseline.

Comments: 16 pages, 4 figures

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

Cite as: arXiv:2606.18308 [cs.LG]

(or arXiv:2606.18308v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.18308

arXiv-issued DOI via DataCite

Submission history

From: Zijie Meng [view email] [v1] Tue, 16 Jun 2026 07:41:43 UTC (1,142 KB)

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