47 citations · 57 across the 9 of their papers we have counts for
10 papers
Deep reinforcement learning for process design: Review and perspective
Qinghe Gao, Artur M. Schweidtmann
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial i…
Robust Reinforcement Learning through Efficient Adversarial Herding
Juncheng Dong, Hao-Lun Hsu, Qitong Gao +2
Although reinforcement learning (RL) is considered the gold standard for policy design, it may not always provide a robust solution in various scenarios. This can result in severe…
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease Treatment
Qitong Gao, Stephen L. Schimdt, Afsana Chowdhury +6
Deep brain stimulation (DBS) has shown great promise toward treating motor symptoms caused by Parkinson's disease (PD), by delivering electrical pulses to the Basal Ganglia (BG) re…
Transfer learning for process design with reinforcement learning
Qinghe Gao, Haoyu Yang, Shachi M. Shanbhag +1
Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, rein…
Variational Latent Branching Model for Off-Policy Evaluation
Qitong Gao, Ge Gao, Min Chi +1
Model-based methods have recently shown great potential for off-policy evaluation (OPE); offline trajectories induced by behavioral policies are fitted to transitions of Markov dec…
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
Laura Stops, Roel Leenhouts, Qinghe Gao +1
Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical proces…
Exploring More When It Needs in Deep Reinforcement Learning
Youtian Guo, Qi Gao
We propose a exploration mechanism of policy in Deep Reinforcement Learning, which is exploring more when agent needs, called Add Noise to Noise (AN2N). The core idea is: when the…
Gradient Importance Learning for Incomplete Observations
Qitong Gao, Dong Wang, Joshua D. Amason +6
Though recent works have developed methods that can generate estimates (or imputations) of the missing entries in a dataset to facilitate downstream analysis, most depend on assump…
Learning-Based Vulnerability Analysis of Cyber-Physical Systems
Amir Khazraei, Spencer Hallyburton, Qitong Gao +2
This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g.,…
Learning Monotone Dynamics by Neural Networks
Yu Wang, Qitong Gao, Miroslav Pajic
Feed-forward neural networks (FNNs) work as standard building blocks in applying artificial intelligence (AI) to the physical world. They allow learning the dynamics of unknown phy…