Cooperative multi-agent reinforcement learning with entangled state representations and copula-based action coordination for urban navigation
The findings demonstrate that combining relational state representations, copula-based dependence modeling, and shared rewards improves coordination, robustness, and stability in multi-agent navigation tasks.