Live Virtual Meeting Link

Meeting Location:
Storey Innovation Center 1400
 

BIO: Dr. Qi Zhang is an assistant professor of the Computer Science and Engineering department and the Artificial Intelligence Institute at the University of South Carolina. He got his Ph.D. from the Computer Science and Engineering department at the University of Michigan. His research aims for solutions for coordinating systems of decision-making agents operating in uncertain, dynamic environments. As hand-engineered solutions for such environments often fall short, He uses ideas from planning and reinforcement learning to develop and analyze algorithms that autonomously coordinate agents in an effective, trustworthy, and communication-efficient manner. In particular, He has been working on social commitments for trustworthy coordination, communication learning, and language emergence among coordinated agents and applications of (multi-agent) reinforcement learning such as intelligent transportation systems, dialogue systems, etc.

ABSTRACT: This talk will present two recent works on training homogeneous reinforcement learning (RL) agents in two distinct scenarios, respectively. The first scenario considers training a group of homogeneous agents that will be deployed in isolation to perform a single-agent RL task, which finds applications in ensemble RL. The first work develops effective techniques for training such as an ensemble of deep Q-learning agents, which help achieve state-of-the-art policy distillation performance in Atari games and continuous control tasks. The second scenario considers training a group of homogeneous agents to cooperatively perform a multi-agent RL task such as team sports. The second work develops novel techniques that exploit the homogeneity to train the agents in a distributed and communication-efficient manner.