SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning
arXiv:2610.13737v1 Announce Type: cross Abstract: Cooperative multi-agent tasks often impose constraints on a shared objective or collective safety. A team level violation cost function can measure this constraint outcome, but does not distinguish which agents' actions most strongly influence that violation. We present SafeDiSTL, a constrained multi-agent reinforcement learning framework that allows for credit assignment even with joint constraint measurements. We use Signal Temporal Logic (STL…