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I am a 1st year PhD student of ECE department, Northwestern University advised by Qi Zhu, and I also work closely with Zhaoran Wang and Chao Huang. Before Northwestern, I did my undergad on Applied Math and Computer Science at UC Berkeley, where I was advised by Sanjit A. Seshia. I had experience on Ubiquitous Computing and Novel sensing and have been fortunately advised by Xing-Dong Yang, Teng Han, and Tian Feng. I'm intertested in combining techniques from machine learning, control theory, and formal method to enfore safety and robustness of the large-scale cyber-physics systems. I'm also broadly interested in Generative Models, Human Factor, and trending new technologies. Email / CV / Google Scholar / DBLP / Github |
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[04/2024] Our paper "State-wise Safe RL With Pixel Observations" is accepted to L4DC 2024 [03/2024] One paper accepted to LLMAgent@ICLR 2024 [09/2023] I will start my PhD journey at ECE department, Northwestern University |
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Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Chao Huang, ICML 2024 arXiv Auxiliary-Delayed Reinforcement Learning (AD-RL) leverages an auxiliary short-delayed task to accelerate the learning on a long-delayed task without compromising the performance in stochastic environments. |
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Simon Sinong Zhan, Yixuan Wang, Qingyuan Wu, Ruochen Jiao, Chao Huang, Qi Zhu, L4DC 2024 arXiv / code and demos In this paper, we propose a novel pixel-observation safe RL algorithm that efficiently encodes state-wise safety constraints with unknown hazard regions through the introduction of a latent barrier function learning mechanism. |
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Yixuan Wang, Simon Sinong Zhan, Ruochen Jiao, Zhilu Wang, Wanxin Jin, Zhuoran Yang, Zhaoran Wang, Chao Huang, Qi Zhu, ICML 2023 arXiv / code A safe RL approach that can jointly learn the environment and optimize the control policy, while effectively avoiding unsafe regions with safety probability optimization. |
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Yixuan Wang*, Simon Sinong Zhan*, Zhilu Wang, Chao Huang, Zhaoran Wang, Zhuoran Yang, Qi Zhu, ICCPS 2023 arXiv / code A framework that jointly conducts reinforcement learning and formal verification by formulating and solving a novel bilevel optimization problem, which is end-to-end differentiable by the gradients from the value function and certificates formulated by linear programs and semi-definite programs. |
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MARS: a toolchain for Modeling, Analyzing and veRifying hybrid Systems |
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