1 code implementation • 29 Jun 2022 • Christopher Yeh, Jing Yu, Yuanyuan Shi, Adam Wierman
Voltage control generally requires accurate information about the grid's topology in order to guarantee network stability.
no code implementations • 3 Jun 2022 • Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar
However, current reinforcement learning (RL) methods lack stabilization guarantees, which limits their applicability for the control of safety-critical systems.
1 code implementation • 1 Jun 2022 • Wenqi Cui, Yan Jiang, Baosen Zhang, Yuanyuan Shi
We explicitly characterize the stability conditions and engineer neural networks that satisfy them by design.
1 code implementation • 14 Dec 2021 • Kevin Huang, Sahin Lale, Ugo Rosolia, Yuanyuan Shi, Anima Anandkumar
It then uses the top trajectories as initialization for gradient descent and applies gradient updates to each of these trajectories to find the optimal action sequence.
no code implementations • 30 Nov 2021 • Yize Chen, Yuanyuan Shi, Daniel Arnold, Sean Peisert
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.
no code implementations • 16 Nov 2021 • Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, Anima Anandkumar
In this work we propose polymatrix competitive gradient descent (PCGD) as a method for solving general sum competitive optimization involving arbitrary numbers of agents.
1 code implementation • NeurIPS 2021 • Yujia Huang, huan zhang, Yuanyuan Shi, J Zico Kolter, Anima Anandkumar
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 computing a global bound on its Lipschitz constant.
no code implementations • 18 Oct 2021 • Alexander Pan, Yongkyun Lee, huan zhang, Yize Chen, Yuanyuan Shi
Due to the proliferation of renewable energy and its intrinsic intermittency and stochasticity, current power systems face severe operational challenges.
1 code implementation • 11 Oct 2021 • Yize Chen, Daniel Arnold, Yuanyuan Shi, Sean Peisert
Case studies performed on both voltage regulation and topology control tasks demonstrated the potential vulnerabilities of the standard reinforcement learning algorithms, and possible measures of machine learning robustness and security are discussed for power systems operation tasks.
no code implementations • 30 Sep 2021 • Yuanyuan Shi, Guannan Qu, Steven Low, Anima Anandkumar, Adam Wierman
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems.
no code implementations • 2 Sep 2021 • 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 demand measurements and incentive signals.
no code implementations • 29 Apr 2021 • Guannan Qu, Yuanyuan Shi, Sahin Lale, Anima Anandkumar, Adam Wierman
In this work, we propose an efficient online control algorithm, COvariance Constrained Online Linear Quadratic (COCO-LQ) control, that guarantees input-to-state stability for a large class of LTV systems while also minimizing the control cost.
no code implementations • 14 Sep 2020 • Yuanyuan Shi, Baosen Zhang
This is the first result (to the best of our knowledge) on the convergence property of learning algorithms with continuous action spaces that do not fall in the no-regret class.
1 code implementation • 20 Mar 2020 • Liyuan Zheng, Yuanyuan Shi, Lillian J. Ratliff, Baosen Zhang
This paper focuses on finding reinforcement learning policies for control systems with hard state and action constraints.
no code implementations • ICLR 2020 • Daniel J. Mankowitz, Nir Levine, Rae Jeong, Yuanyuan Shi, Jackie Kay, Abbas Abdolmaleki, Jost Tobias Springenberg, Timothy Mann, Todd Hester, Martin Riedmiller
We provide a framework for incorporating robustness -- to perturbations in the transition dynamics which we refer to as model misspecification -- into continuous control Reinforcement Learning (RL) algorithms.
no code implementations • COLING 2016 • Naitong Yu, Minlie Huang, Yuanyuan Shi, Xiaoyan Zhu
The main idea of our method is to leverage phrase properties to choose a subset of optimal phrases for generating the final summary.