Guan-Ting Liu

I am a Ph.D. student at the Graduate Institute of Networking and Multimedia at National Taiwan University in Taipei, Taiwan. I am advised by Pu-Jen Cheng, Iris Hui-Ru Jiang, and Shao-Hua Sun. I'm focusing on Reinforcement Learning, Machine Learning, and Electronic Design Automation. I am funded by the TSMC Ph.D. Scholarship.

I completed my bachelor's degree in Electronic Engineering with cross-discipline in Computer Science at NCTU (NYCU by now). I've received the Academic Excellence Award of the Department of Electronic Engineering and the Excellent Work Award of Big Data Analytics for Semiconductor Manufacturing.

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I'm interested in Machine Learning, Reinforcement Learning(RL), and Electronic Design Automation (EDA). Much of my research is about interpretability and generalization of RL and applying machine learning techniques to solve EDA problems.

This work proposes Program Machine Policies (POMPs), which bridge the advantages of programmatic RL and state machine policies, allowing for the representation of complex behaviors and the address of long-term tasks.

Our proposed hierarchical programmatic reinforcement learning (HPRL) framework can produce program policies that describe out-of-distributionally complex behaviors and directly assign credits to programs that induce desired behaviors.

We are the first to generate SRAFs using reinforcement learning to address SRAF interference and produce mask-rule-compliant results directly.

We propose a novel technique that regularizes representation learning by comparing state embedding similarities across different state-action pairs.