Learning and exploiting shaped reward models for large scale multiagent RL

• Main Authors: ARAMBAM JAMES SINGH, KUMAR, Akshat, LAU, Hoong Chuin
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Model Representation And Learning Domain Models For Planning; Multi-agent Planning And Learning; Artificial Intelligence and Robotics; Operations Research, Systems Engineering and Industrial Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6126; https://ink.library.smu.edu.sg/context/sis_research/article/7129/viewcontent/ICAPS_2021___Learning_Shaped_Reward_Models_for_Large_Scale_Multiagent_RL.pdf
• Institution: Singapore Management University

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in...