Publications

For the updated list of publications, please see my google scholar page.

Preprints

Wenqi Cui, Yan Jiang, Baosen Zhang, and Yuanyuan Shi, "Structured Neural-PI Control for Networked Systems: Stability and Steady-State Optimality Guarantees", arXiv:2206.00261. [PDF]

Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, and Anima Anandkumar, "KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems", arXiv:2206.01704. [PDF]

Jeffrey Ma, Alistair Letcher, Florian Schäfer, Yuanyuan Shi, and Anima Anandkumar, "Polymatrix Competitive Gradient Descent", arXiv:2111.08565. [PDF]

2022

Christopher Yeh, Jing Yu, Yuanyuan Shi, and Adam Wierman, "Robust online voltage control with an unknown grid topology", ACM e-Energy 2022, 2022. (Best Paper Finalist)


Yuexin Bian, Ningkun Zheng, Yang Zheng, Bolun Xu, and Yuanyuan Shi, "Demand response model identification and behavior forecast with OptNet: a gradient-based approach", ACM e-Energy 2022, 2022.


Yuanyuan Shi*, Guannan Qu*, Steven Low, Anima Anandkumar, and Adam Wierman, "Stability Constrained Reinforcement Learning for Real-Time Voltage Control", American Control Conference (ACC), 2022.


Songyang Han, He Wang, Sanbao Su, Yuanyuan Shi, and Fei Miao, "Stable and Efficient Shapley Value-Based Reward Reallocation for Multi-Agent Reinforcement Learning of Autonomous Vehicles", IEEE International Conference on Robotics and Automation (ICRA), 2022.

2021

Meng Qi*, Yuanyuan Shi*, Yongzhi Qi, Chenxin Ma, Rong Yuan, Di Wu, and Zuojun M. Shen, "A Practical End-to-End Inventory Management Model with Deep Learning", Management Science, forthcoming. [PDF]


Yujia Huang, Huan Zhang, Yuanyuan Shi, Zico Kolter, and Anima Anandkumar, "Training Certiably Robust Neural Networks with Efficient Local Lipschitz Bounds", in Thirty-Fifth Conference on Neural Information Processing Systems (NeurIPS), 2021. [PDF] [Code]


Alexander Pan, Yongkyun (Daniel) Lee, Yize Chen, Huan Zhang, and Yuanyuan Shi, "Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training", in Real-World Reinforcement Learning Workshop, ICML, 2021. [PDF][Code]


Guannan Qu*, Yuanyuan Shi*, Sahin Lale*, Anima Anandkumar, and Adam Wierman, "Stable Online Control of Linear Time-Varying Systems", in 3rd Annual Learning for Dynamics & Control Conference (L4DC), 2021. [PDF]


Liyuan Zheng, Yuanyuan Shi, Lillian Ratli, and Baosen Zhang, "Safe Reinforcement Learning of Control-Affine Systems with Vertex Networks", in 3rd Annual Learning for Dynamics & Control Conference (L4DC), 2021. [PDF][Code]

2020

Yuanyuan Shi, and Baosen Zhang, "Learning in Cournot Games with Limited Information Feedback", in Proceedings of IEEE Conference on Decision and Control (CDC), 2020. [PDF][Code]

Daniel J. Mankowitz, Nir Levine, Rae Jeong, Abbas Abdolmaleki, Jost Tobias Springenberg, Yuanyuan Shi, Jackie Kay, Todd Hester, Timothy Mann, Martin Riedmiller, “Robust Reinforcement Learning for Continuous Control with Model Misspecification’’, International Conference on Learning Representations (ICLR), 2020. [PDF]

Yize Chen, Yuanyuan Shi, and Baosen Zhang, "Data-driven Optimal Voltage Regulation using Input Convex Neural Networks", Electric Power Systems Research, 2020. [PDF]

2019

Yuanyuan Shi, Bolun Xu, Yushi Tan, Daniel Kirschen, and Baosen Zhang, “Optimal Battery Control Under Cycle Aging Mechanisms in Pay for Performance Settings’’, IEEE Transactions on Automatic Control, 2019. [PDF] [Code]

Yuanyuan Shi, Kai Xiao, Daniel J. Mankowitz, Rae Jeong, Nir Levine, Sven Gowal, Timothy Mann, and Todd Hester, “Data-Driven Robust Reinforcement Learning for Continuous Control’’, Safety and Robustness in Decision Making Workshop, Neural Information Processing Systems (NeurIPS), 2019. [PDF] [Poster]

Kai Xiao, Sven Gowal, Todd Hester, Rae Jeong, Daniel J. Mankowitz, Yuanyuan Shi, and T.W. Weng, Learning Neural Dynamics Simulators With Adversarial Specification Training’’, Safety and Robustness in Decision Making Workshop, Neural Information Processing Systems (NeurIPS), 2019. [PDF] [Poster]

Yize Chen*, Yuanyuan Shi*, and Baosen Zhang, “Optimal Control Via Neural Networks: A Convex Approach’’, International Conference on Learning Representations (ICLR), 2019. [PDF] [Code]

2018 and earlier

Bolun Xu, Yuanyuan Shi, Daniel Kirschen, and Baosen Zhang, “Optimal Battery Participation in Frequency Regulation Markets”, IEEE Transactions on Power Systems, 2018. [PDF] [Code]

Yuanyuan Shi, Bolun Xu, Yushi Tan, and Baosen Zhang, “A convex cycle-based degradation model for battery energy storage planning and operation’’, American Control Conference (ACC), 2018 [PDF]

Yuanyuan Shi, Bolun Xu, Di Wang, and Baosen Zhang, “Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains’’, IEEE Transactions on Power Systems, 2017. [PDF] [Code]

Bolun Xu, Yuanyuan Shi, Daniel Kirschen, and Baosen Zhang, “Optimal regulation response of batteries under cycle aging mechanisms”, IEEE Conference on Decision and Control (CDC), 2017 [PDF]

Yize Chen, Yuanyuan Shi, and Baosen Zhang. “Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks’’, Asilomar Conference, 2017. [PDF]

Yuanyuan Shi, Bolun Xu, Baosen Zhang, and Di Wang, “Leveraging energy storage to optimize data center electricity cost in emerging power markets”, Seventh International Conference on Future Energy Systems, ACM (e-Energy), 2016. [PDF]

Luowei Zhou, Yuanyuan Shi, Jiangliu Wang, and Pei Yang, “A Balanced Heuristic Mechanism for Multirobot Task Allocation of Intelligent Warehouses’’, Journal of Mathematical Problems in Engineering, 2014. [PDF]