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Shuyuan Wang
Shuyuan Wang completed his PhD at UBC DAIS Lab in 2025 and is currently a Postdoctoral Fellow. His research focuses on the intersection of reinforcement learning and control, developing methods to make RL more efficient by incorporating partial model knowledge.
📚 Program/Degree
Postdoc
| Started 2025
📝 Research
Intersection between reinforcement learning (RL) and control, making RL more efficient
🔗 Website
DAIS Lab Publications
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Conference Proceedings
A modular framework for stabilizing deep reinforcement learning control
In Proceedings of the 22nd IFAC World Congress. 2023 [PDF] [Slides] [Video] [arXiv] -
Conference Proceedings
Reinforcement Learning with Partial Parametric Model Knowledge
In Proceedings of the 22nd IFAC World Congress. 2023 [PDF] [Video] [arXiv] -
Conference Proceedings
Deep Hankel matrices with random elements
In Proceedings of the 6th Annual Learning for Dynamics & Control Conference (L4DC). 2024 [PDF] -
Conference Proceedings
Guiding Reinforcement Learning with Incomplete System Dynamics
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024, To Appear). 2024 [PDF] [Video] [Presentation] [arXiv] -
Conference Proceedings
Empowering Neural Networks with Control and Planning Abilities
In NeurIPS 2024 Workshop on Behavioral ML (non-archival). 2024 [PDF] -
Journal Paper
Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior
Automatica. 2024 [PDF] [Code] [arXiv] -
Conference Proceedings
DiLQR: Differentiable Iterative Linear Quadratic Regulator via Implicit Differentiation
Poster session, Proceedings of the 42nd International Conference on Machine Learning (ICML 2025). 2025 [PDF] [Code] [arXiv]
DAIS Lab Member? Submit a pull request or join the @daisubc organization on GitHub to update your profile - Shuyuan Wang.md.