Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning
This work introduces a hierarchical framework for safe multi-agent navigation. It combines goal-conditioned Reinforcement Learning with Conflict-Based Search (CBS) to plan paths. The system learns a safe policy while estimating cumulative distance and safety using self-training, then uses these estimates to build a graph for CBS planning, resulting in safer, lower-cost paths.