Title: Multiagent Reinforcement Learning For Large Agent Population
Speaker: Dr. Arambam James Singh, Postdoctoral Fellow at Nanyang Technological University (NTU) in Singapore.
Day, Date, and Time: Friday, 2nd February 2024, 11:00 am to 12:00 Noon
Venue: Seminar room IE 211, Second Floor, IEOR Building
Abstract: In today's world, many sectors, such as healthcare, transportation, etc., are rapidly digitizing their industrial processes. This presents a significant opportunity for developing next-generation artificial intelligence systems with multiple agents that can operate effectively at scale. Multiagent reinforcement learning is a field of study that focuses on solving problems in multiagent systems. In this seminar, I will share my research that addresses critical challenges such as scalability and credit assignment problems in large-scale multiagent systems, specifically in a cooperative environment. My proposed methodology is built around aggregate information, which offers a high level of scalability. Importantly, the dimension of key statistics needed for training the multiagent policies does not change, even if the number of agents increases significantly, making it an effective solution for large-scale complex systems.
Bio: Dr. Arambam James Singh received his Ph.D. in Computer Science from the School of Computing & Information Systems at Singapore Management University (SMU) in Aug. 2021. He has undertaken his first postdoctoral fellowship at the National University of Singapore (NUS) and is currently pursuing his second postdoctoral fellowship at Nanyang Technological University (NTU) in Singapore. His research interest primarily focuses on reinforcement learning and multiagent reinforcement learning.