Search Results for author: Yuanyuan Shi

Found 9 papers, 1 papers with code

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

Understanding the Safety Requirements for Learning-based Power Systems Operations

no code implementations11 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.

Decision Making

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.

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

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.

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

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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