Publications

You can also find my publications on my Google Scholar profile.

Preprints

• Ziyi Zhang, Yorie Nakahira, Guannan Qu, “Learning to Stabilize LTI Systems on a Single Trajectory under Stochastic Noise”, under review.
• Emile Anand, Guannan Qu, “Efficient Reinforcement Learning for Global Decision Making in the Presence of Local Agents at Scale”, under review. (available as arXiv preprint arXiv:2403.00222, 2024).
• Han Xu, Jialin Zheng, Guannan Qu, A Scalable Network-Aware Multi-Agent Reinforcement Learning Framework for Decentralized Inverter-based Voltage Control, arXiv preprint arXiv:2312.04371 (2023).
• Ziyi Zhang, Guannan Qu, Yorie Nakahira, “Fast Bandit-based Policy Adaptation in Diverse Environments”, under review. Preprint available here

Selected Conference Publications

• Alex Deweese, Guannan Qu, “Interdependent Multi-Agent MDP: Theoretical Framework for Decentralized Agents with Dynamic Local Dependencies”, ICML 2024
• Junxuan Shen, Adam Wierman, Guannan Qu, “Combining Model-based Controller and ML Advice via Convex Reparameterization”, 6th Learning for Dynamics and Control 2024.
• Zeji Yi, Chaoyi Pan, Guanqi He, Guannan Qu, Guanya Shi, CoVO-MPC: Theoretical Analysis of Sampling-based MPC and Optimal Covariance Design, 6th Learning for Dynamics and Control 2024.
• Eric Xu, Guannan Qu, Stability and Regret bounds on Distributed Truncated Predictive Control for Networked Dynamical Systems, American Control Conference 2024.
• Neharika Jali, Guannan Qu, Weina Wang, Gauri Joshi, Efficient Reinforcement Learning for Routing Jobs in Heterogeneous Queueing Systems, AISTATS 2024.
• Eric Xu and Guannan Qu, Natural Policy Gradient Preserves Spatial Decay Properties for Control of Networked Dynamical Systems, in IEEE Conference on Decision and Control, 2023.
• Songyuan Zhang, Yumeng Xiu, Guannan Qu, Chuchu Fan, Compositional Neural Certificates for Networked Dynamical Systems, 5th Learning for Dynamics and Control Conference, 2023 (oral presentation).
• Yizhou Zhang*, Guannan Qu*, Pan Xu*, Yiheng Lin, Zaiwei Chen, Adam Wierman, Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning, ACM SIGMETRICS 2023. (* equal contribution)
• Yang Hu, Adam Wierman, Guannan Qu, On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory, NeurIPS 2022.
• Yiheng Lin, Yang Hu, Guannan Qu, Tongxin Li, Adam Wierman, Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity, NeurIPS 2022.
• Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low, Robustness and Consistency in Linear Quadratic Control with Predictions , ACM SIGMETRICS 2022.
• Yiheng Lin, Judy Gan, Guannan Qu, Yash Kanoria, Adam Wierman, Decentralized Online Convex Optimization in Networked Systems, ICML 2022.
• Yiheng Lin*, Yang Hu*, Haoyuan Sun*, Guanya Shi*, Guannan Qu*, Adam Wierman, Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems, NeurIPS 2021 spotlight. (* denotes equal contribution)
• Yiheng Lin, Guannan Qu, Longbo Huang, Adam Wierman, Multi-Agent Reinforcement Learning in Stochastic Networked Systems, NeurIPS 2021.
• Guannan Qu, Yiheng Lin, Adam Wierman, Na Li, Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward, NeurIPS 2020.
• Guannan Qu and Adam Wierman, Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-learning, Conference on Learning Theory (COLT) 2020.
• Guannan Qu, Adam Wierman and Na Li, Scalable Reinforcement Learning of Localized Policies for Multi-Agent Networked Systems, 2nd Learning for Dynamics and Control Conference, oral presentation (top 10%).

Journal Publications

• Jie Feng, Yuanyuan Shi, Guannan Qu, Steven H Low, Anima Anandkumar, Adam Wierman, Stability constrained reinforcement learning for decentralized real-time voltage control, accepted to IEEE Transactions on Control of Network Systems.
• Sungho Shin, Yiheng Lin, Guannan Qu, Adam Wierman, Mihai Anitescu, Near-Optimal Distributed Linear-Quadratic Regulator for Networked Systems, SIAM Journal on Control and Optimization, vol. 61, no. 3, 2023.
• Tongxin Li, Ruixiao Yang, Guannan Qu, Yiheng Lin, Adam Wierman, Steven H Low, Certifying Black-Box Policies With Stability for Nonlinear Control, IEEE Open Journal of Control Systems, vol. 2, 2023.
• Xin Chen, Guannan Qu, Yujie Tang, Steven Low, Na Li, Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges , IEEE Transactions on Smart Grid, vol. 13, no. 4, July 2022.
• Tongxin Li, Ruixiao Yang, Guannan Qu, Guanya Shi, Chenkai Yu, Adam Wierman, Steven Low, ``Robustness and Consistency in Linear Quadratic Control with Predictions,’’ Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 6, no. 1, 2022.
• Guannan Qu, Adam Wierman, Na Li, Scalable Reinforcement Learning for Multi-Agent Networked Systems, Operations Research, vol. 70, no. 6, 2022.
• Niloy Patari, Anurag K Srivastava, Guannan Qu, Na Li, Distributed Voltage Control for Three-Phase Unbalanced Distribution Systems with DERs and Practical Constraints, IEEE Transactions on Industry Applications, vol. 57, no. 6, Nov.-Dec. 2021
• Yingying Li, Guannan Qu and Na Li, Using predictions in online optimization with switching costs: Algorithms and Fundamental Limits, IEEE Transactions on Automatic Control, vol. 66, no. 10, pp. 4761 - 4768, Oct. 2021.
• Yujie Tang, Guannan Qu and Na Li, Semi-Global Exponential Stability of Primal-Dual Gradient Dynamics for Constrained Convex Optimization, Systems & Control Letters, vol. 144, Oct. 2020.
• Sindri Magnússon, Guannan Qu and Na Li, Distributed Optimal Voltage Control with Asynchronous and Delayed Communication, IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3469 - 3482, July 2020.
• Guannan Qu and Na Li, Accelerated Distributed Nesterov Gradient Descent, IEEE Transactions on Automatic Control, vol. 65, no. 6, pp. 2566 - 2581, June 2020.
• Guannan Qu and Na Li, Optimal Distributed Feedback Voltage Control under Limited Reactive Power, IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 315 - 331, January 2020.
• Sindri Magnússon, Guannan Qu, Carlo Fischione, Na Li, Voltage Control Using Limited Communication, IEEE Transactions on Control of Network Systems, vol. 6, no. 3, pp 993-1003, Sept. 2019.
• Guannan Qu, David Brown, and Na Li, Distributed Greedy Algorithm for Multi-Agent Task Assignment Problem with Submodular Utility Functions, Automatica, vol 105, pp. 206-215, July 2019.
• Guannan Qu and Na Li, On the Exponential Stability of Primal-Dual Gradient Dynamics, IEEE Control Systems Letters, vol. 3, no. 1, pp. 43-48, Jan. 2019.
• Xiaoqi Tan, Guannan Qu, Bo Sun, Na Li, and Danny H.K. Tsang, Optimal Scheduling of Battery Charging Station Serving Electric Vehicles Based on Battery Swapping, IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1372-1384, March 2019.
• Guannan Qu and Na Li, Harnessing Smoothness to Accelerate Distributed Optimization, IEEE Transactions on Control of Network Systems, vol. 5, no. 3, pp. 1245-1260, Sept. 2018.

Other Conference Publications

• Sahin Lale, Yuanyuan Shi, Guannan Qu, Kamyar Azizzadenesheli, Adam Wierman, Anima Anandkumar, KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Discrete-Time Systems, 62nd IEEE Conference on Decision and Control (CDC), 2023.
• Yuanyuan Shi*, Guannan Qu*, Steven Low, Anima Anandkumar, Adam Wierman, Stability Constrained Reinforcement Learning for Real-Time Voltage Control , American Control Conference 2022. (* denotes equal contribution)
• Guannan Qu*, Yuanyuan Shi*, Sahin Lale*, Anima Anandkumar, Adam Wierman, Stable Online Control of Linear Time-Varying Systems, 3rd Learning for Dynamics and Control Conference. (* denotes equal contribution)
• Guannan Qu, Chenkai Yu, Steven Low, Adam Wierman, Exploiting linear models for model-free nonlinear control: A provably convergent policy gradient approach, in 60th Conference for Decision and Control, 2021.
• Andreas Venzke, Guannan Qu, Steven Low, Spyros Chatzivasileiadis, Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks, IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2020.
• Niloy Patari, Anurag Srivastava, Guannan Qu, Na Li, Distributed Optimal Voltage Control for Three Phase Unbalanced Distribution Systems with DERs, IEEE Industry Applications Annual Meeting, 2020.
• Guannan Qu and Na Li, Exploiting fast decaying and locality in multi-agent mdp with tree dependence structure, IEEE Conference on Decision and Control, 2019.
• Yingying Li, Aoxiao Zhong, Guannan Qu, and Na Li, Online Markov Decision Processes with Time-varying Transition Probabilities and Rewards, ICML 2019 Real-world Sequential Decision Making workshop.
• Yingying Li, Guannan Qu and Na Li, Using predictions in online optimization with switching costs: Algorithms and Fundamental Limits, American Control Conference 2018.
• Guannan Qu and Na Li, An Optimal and Distributed Feedback Voltage Control under Limited Reactive Power, 20th Power System Computation Conference 2018.
• Guannan Qu and Na Li, Accelerated Distributed Nesterov Gradient Descent for Smooth and Convex Functions, in 56th IEEE Conference on Decision and Control, Melbourne, Australia, December 2017.
• Guannan Qu and Na Li, Harnessing Smoothness to Accelerate Distributed Optimization, 55th IEEE Conference on Decision and Control, 2016.
• Guannan Qu and Na Li, Accelerated Distributed Nesterov Gradient Descent for Smooth and Strongly Convex Functions, Allerton Conference on Communication, Control, and Computing, 2016.
• Guannan Qu, Dave Brown and Na Li, Distributed Greedy Algorithm for Satellite Assignment Problem with Submodular Utility Function, 5th IFAC Workshop on Estimation and Control of Networked Systems, 2015.
• Na Li, Guannan Qu, Munther Dahleh, Real-time Decentralized Voltage Control in Distribution Networks, Allerton Conference on Communication, Control and Computing, 2014.

Technical Reports

• Guannan Qu, Na Li, and Munther Dahleh, Real-time Decentralized and Robust Voltage Control in Distribution Networks, arXiv preprint arXiv:1606.08101 (2016).