Search Results for author: Yuanyuan Shi

Found 16 papers, 6 papers with code

Robust Online Voltage Control with an Unknown Grid Topology

1 code implementation29 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.

KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems

no code implementations3 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.

reinforcement-learning

Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees

1 code implementation1 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.

CEM-GD: Cross-Entropy Method with Gradient Descent Planner for Model-Based Reinforcement Learning

1 code implementation14 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.

Continuous Control Model-based Reinforcement Learning +1

SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation

no code implementations30 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.

reinforcement-learning

Polymatrix Competitive Gradient Descent

no code implementations16 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.

Multi-agent Reinforcement Learning

Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds

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.

Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training

no code implementations18 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.

Decision Making reinforcement-learning

Understanding the Safety Requirements for Learning-based Power Systems Operations

1 code implementation11 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.

BIG-bench Machine Learning Decision Making +2

Stability Constrained Reinforcement Learning for Real-Time Voltage Control

no code implementations30 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.

reinforcement-learning

End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning

no code implementations2 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.

Decision Making

Stable Online Control of Linear Time-Varying Systems

no code implementations29 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.

Multi-Agent Reinforcement Learning in Cournot Games

no code implementations14 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.

Continuous Control Multi-agent Reinforcement Learning +1

Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks

1 code implementation20 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.

reinforcement-learning Safe Reinforcement Learning

Robust Reinforcement Learning for Continuous Control with Model Misspecification

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.

Continuous Control reinforcement-learning

Product Review Summarization by Exploiting Phrase Properties

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.

Abstractive Text Summarization Language Modelling

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