SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning

SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning. Reported by arXiv. Bharat Hunt files it under AI Agents and AI Research — the section covering autonomous and tool-using systems, agent frameworks, MCP and orchestration. Open the original report for the full detail.

Written by Bharat Hunt from the headline and the coverage below. The original reporting is the source of truth.