Reinforcement learning for zone based multiagent pathfinding under uncertainty

• Main Authors: LING, Jiajing, GUPTA, Tarun, KUMAR, Akshat
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2020
• Subjects: Autonomous agents; Functions; Multi agent systems; Reinforcement learning; Scheduling; Traffic congestion; Travel time; Artificial Intelligence and Robotics; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/5963; https://ink.library.smu.edu.sg/context/sis_research/article/6966/viewcontent/Reinforcement_Learning_for_Zone_Based_Multiagent_Pathfinding_under_Uncertainty.pdf
• Institution: Singapore Management University

We address the problem of multiple agents finding their paths from respective sources to destination nodes in a graph (also called MAPF). Most existing approaches assume that all agents move at fixed speed, and that a single node accommodates only a single agent. Motivated by the emerging applicatio...