Approximate difference rewards for scalable multigent reinforcement learning

• Main Authors: SINGH, Arambam James, KUMAR, Akshat, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Reinforcement learning; multiagent systems; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6022; https://ink.library.smu.edu.sg/context/sis_research/article/7025/viewcontent/AAMAS_2021_ext_abs.pdf
• Institution: Singapore Management University

We address the problem ofmultiagent credit assignment in a large scale multiagent system. Difference rewards (DRs) are an effective tool to tackle this problem, but their exact computation is known to be challenging even for small number of agents. We propose a scalable method to compute difference rewards based on aggregate information in a multiagent system with large number of agents by exploiting the symmetry present in several practical applications. Empirical evaluation on two multiagent domains - air-traffic control and cooperative navigation, shows better solution quality than previous approaches.