RL-Inspired Pairwise Path Selection for Multi-Agent Navigation
This paper presents an RL (Reinforcement Learning) inspired pairwise path selection framework for multi-agent navigation in an offline setting. Path performance is quantified using interaction metrics derived from artificial potential field (APF)-based collision avoidance, together with overall task completion time. Th...