27 citations · 204 across the 36 of their papers we have counts for
9 papers · 1 filter
CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning
Kevin Huang, Sahin Lale, Ugo Rosolia +2
Current state-of-the-art model-based reinforcement learning algorithms use trajectory sampling methods, such as the Cross-Entropy Method (CEM), for planning in continuous control s…
SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation
Yize Chen, Yuanyuan Shi, Daniel Arnold +1
Fast and safe voltage regulation algorithms can serve as fundamental schemes for achieving a high level of renewable penetration in the modern distribution power grids. Faced with…
Polymatrix Competitive Gradient Descent
Jeffrey Ma, Alistair Letcher, Florian Schäfer +2
Many economic games and machine learning approaches can be cast as competitive optimization problems where multiple agents are minimizing their respective objective function, which…
Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds
Yujia Huang, Huan Zhang, Yuanyuan Shi +2
Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by…
Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training
Alexander Pan, Yongkyun Lee, Huan Zhang +2
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges. Data-driven decision-makin…
Understanding the Safety Requirements for Learning-based Power Systems Operations
Yize Chen, Daniel Arnold, Yuanyuan Shi +1
Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of…
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Yuanyuan Shi, Guannan Qu, Steven Low +2
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world powe…
End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning
Yuanyuan Shi, Bolun Xu
This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net dem…
Stable Online Control of Linear Time-Varying Systems
Guannan Qu, Yuanyuan Shi, Sahin Lale +2
Linear time-varying (LTV) systems are widely used for modeling real-world dynamical systems due to their generality and simplicity. Providing stability guarantees for LTV systems i…