Zheqing (Bill) Zhu

ENGINEERING MANAGER | MENLO PARK, UNITED STATES

Zheqing (Bill) is the engineering manager for the Applied Reinforcement Learning group within Meta AI. The Applied Reinforcement Learning group aims to bring the state-of-the-art reinforcement learning technology to life by pushing the frontier of industry-ready reinforcement learning technologies. The team owns Meta's reinforcement learning stack and spans across dozens of product reinforcement learning usecases. Our application and research areas include but are not limited to recommender system, user contextual understanding, control mechanisms, and many more. The team also developed and currently owns Meta's open source reinforcement learning platform ReAgent (reagent.ai).

Bill's personal research interest lies in bridging the gap between theoretical reinforcement learning with practical applications. Some of the reinforcement learning topics that Bill is particularly interested in are improving sample complexity, handling nonstationarity, generalization of value functions, and representation of agent-environment history.

Zheqing's Work

Zheqing's Publications

January 06, 2024

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Learning to bid and rank together in recommendation systems

Geng Ji, Wentao Jiang, Jiang Li, Fahmid Morshed Fahid, Zhengxing Chen, Yinghua Li, Jun Xiao, Chongxi Bao, Zheqing (Bill) Zhu

January 06, 2024

September 12, 2023

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Optimizing Long-term Value for Auction-Based Recommender Systems via On-Policy Reinforcement Learning

Bill Zhu, Alex Nikulkov, Dmytro Korenkevych, Fan Liu, Jalaj Bhandari, Ruiyang Xu, Urun Dogan

September 12, 2023

September 12, 2023

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Scalable Neural Contextual Bandit for Recommender Systems

Bill Zhu, Benjamin Van Roy

September 12, 2023

September 06, 2023

RANKING AND RECOMMENDATIONS

REINFORCEMENT LEARNING

Deep Exploration for Recommendation Systems

Bill Zhu, Benjamin Van Roy

September 06, 2023

June 15, 2023

REINFORCEMENT LEARNING

IQL-TD-MPC: Implicit Q-Learning for Hierarchical Model Predictive Control

Yingchen Xu, Bobak Hashemi, Lucas Lehnert, Rohan Chitnis, Urun Dogan, Zheqing (Bill) Zhu, Olivier Delalleau

June 15, 2023