Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract)


  • Feng Chen Nanjing University
  • Chenghe Wang Nanjing University
  • Fuxiang Zhang Nanjing University
  • Hao Ding Nanjing University
  • Qiaoyong Zhong Hikvision Research Institute
  • Shiliang Pu Hikvision Research Institute
  • Zongzhang Zhang Nanjing University



Reinforcement Learning, Multi-Agent Path Finding, Multi-Agent System


Multi-agent pathfinding (MAPF) is essential to large-scale robotic coordination tasks. Planning-based algorithms show their advantages in collision avoidance while avoiding exponential growth in the number of agents. Reinforcement-learning (RL)-based algorithms can be deployed efficiently but cannot prevent collisions entirely due to the lack of hard constraints. This paper combines the merits of planning-based and RL-based MAPF methods to propose a deployment-efficient and collision-free MAPF algorithm. The experiments show the effectiveness of our approach.




How to Cite

Chen, F., Wang, C., Zhang, F., Ding, H., Zhong, Q., Pu, S., & Zhang, Z. (2023). Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16182-16183.