University of Michigan
Zeyu Zheng is a PhD candidate at the University of Michigan working with Prof. Satinder Singh.
Zeyu is interested in reinforcement learning. His research has been focused on learning various forms of knowledge from the environment such as intrinsic rewards, temporal credit assignment mechanisms, and state representations. His long-term research goal is to build adaptive agents that learn effectively and efficiently from their experience in real-world environments.
Zeyu received his BS in Computer Science from Peking University in 2017. During his undergraduate study, Zeyu worked on parallel and distributed computing. His work won the best paper award at ACM SIGMOD in 2017.
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