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

Researchers introduced TRIDENT, the first multi-agent reinforcement learning framework that co-designs three components to cancel biases from hybrid discrete-continuous actions, safety constraints, and physics-governed dynamics. TRIDENT achieves a 95.5% reduction in training-time violations over MADDPG and 76.3% over MACPO while improving reward by 13.5% over unconstrained baselines in multi-UAV, autonomous intersection, and hybrid SMAC tasks.

arXiv:2606.18308v1 Announce Type: new 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.