Zhaoyang Zhu
Algorithm Engineer at 阿里巴巴集团
Old Toronto, Ontario, Canada
Summary
🤩
Rockstar🎓
Top SchoolZhaoyang Zhu is an algorithm engineer with five years’ experience building and applying reinforcement learning solutions at leading Chinese tech firms including Alibaba and Baidu, and earlier engineering roles at GUANDATA. Based in Toronto, he combines a strong academic foundation in applied statistics and information technology from the University of Toronto with hands-on RL engineering—contributing DQN and DDQN implementations, experiments, and docs to the widely used PaddlePaddle/PARL repository. He focuses on practical, high-performance RL for Atari-like environments, demonstrating both code-level rigor and attention to empirical evaluation. Comfortable in production-oriented teams, he brings a blend of research-driven methods and engineering discipline to large-scale model and agent design. An avid contributor to open-source RL tooling, he pairs fast prototyping instincts (gogogoGo!) with measurable experiment results.
5 years of coding experience
1 year of employment as a software developer
Master of Engineering - MEng, Information Technology, Master of Engineering - MEng, Information Technology at University of Toronto
Github Skills (7)
dqn10
machine-learning10
python10
reinforcement-learning10
large-scale8
parallelization8
tensorflow7
Programming languages (2)
Github contributions (5)
PaddlePaddle/PARL
May 2021 - Jul 2021
A high-performance distributed training framework for Reinforcement Learning
Role in this project:
ML Engineer
ML EngineerContributions:19 commits, 19 PRs, 26 pushes in 2 months
Contributions summary:Zhaoyang significantly contributed to implementing and refining reinforcement learning examples within the repository. Their work includes adding and refactoring code for Deep Q-Network (DQN) and Double DQN (DDQN) implementations, specifically for Atari environments. They also added experiment results and documentation, demonstrating a focus on practical application and performance evaluation of these RL algorithms. Their commits show an understanding of model architecture and agent design within the reinforcement learning framework.
reinforcement-learningdeep-reinforcement-learningmachine-learningdistributed-trainingtraining
swag1ong/swag1ong.github.io
May 2021 - Feb 2024
Contributions:178 pushes, 1 branch, 1 comment in 2 years 10 months
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