ML PhD student at Georgia Tech
My research interests lie in the intersection of optimization, reinforcement learning, and robotics. I develop algorithms to efficiently train robots, ideally with theoretical guarantee.
I am co-advised by Prof. Anqi Wu and Prof. Ye Zhao at Georgia Tech. Additionally, I had the privilege to collaborate with Prof. George Lan on stochastic optimization & RL.
Previously, I contributed to the development team of optimization solvers for large-scale linear programming problems at Chinese University of Hong Kong, Shenzhen (CUHKSZ), where I spent my undergrad years in computer science and engineering.
Publication
Reinforcement Learning:
Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer. Feb 2023. under review.
Feiyang Wu, Zhaoyuan Gu, Ye Zhao, Anqi Wu
Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning. Sept 2023, ICRA 2024.
Feiyang Wu, Zhaoyuan Gu, Hanran Wu, Anqi Wu, Ye Zhao
Inverse Reinforcement Learning with the Average Reward Criterion, NeurIPS 2023
Feiyang Wu, Jingyang Ke, Anqi Wu
Stochastic first-order methods for average-reward Markov decision processes, Sept 2022, under revision. Mathematics of OR.
Tianjiao Li, Feiyang Wu, Guanghui Lan
Computer Vision:
SAniHead: Sketching Animal-Like 3D Character Heads Using a View-Surface Collaborative Mesh Generative Network, 2020, IEEE Transactions on Visualization and Computer Graphics (TVCG).
Dong Du, Xiaoguang Han, Hongbo Fu, Feiyang Wu, Yizhou Yu, Shuguang Cui, Ligang Liu