564 citations · 1.6k across the 27 of their papers we have counts for
29 papers
Compositional Neural Certificates for Networked Dynamical Systems
Songyuan Zhang, Yumeng Xiu, Guannan Qu +1
Developing stable controllers for large-scale networked dynamical systems is crucial but has long been challenging due to two key obstacles: certifiability and scalability. In this…
Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning
Yizhou Zhang, Guannan Qu, Pan Xu +3
We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of thei…
Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity
Yiheng Lin, Yang Hu, Guannan Qu +2
We study Model Predictive Control (MPC) and propose a general analysis pipeline to bound its dynamic regret. The pipeline first requires deriving a perturbation bound for a finite-…
Decentralized Online Convex Optimization in Networked Systems
Yiheng Lin, Judy Gan, Guannan Qu +2
We study the problem of networked online convex optimization, where each agent individually decides on an action at every time step and agents cooperatively seek to minimize the to…
KCRL: Krasovskii-Constrained Reinforcement Learning with Guaranteed Stability in Nonlinear Dynamical Systems
Sahin Lale, Yuanyuan Shi, Guannan Qu +3
Learning a dynamical system requires stabilizing the unknown dynamics to avoid state blow-ups. However, current reinforcement learning (RL) methods lack stabilization guarantees, w…
Equipping Black-Box Policies with Model-Based Advice for Stable Nonlinear Control
Tongxin Li, Ruixiao Yang, Guannan Qu +3
Machine-learned black-box policies are ubiquitous for nonlinear control problems. Meanwhile, crude model information is often available for these problems from, e.g., linear approx…
On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory
Yang Hu, Adam Wierman, Guannan Qu
Stabilizing an unknown dynamical system is one of the central problems in control theory. In this paper, we study the sample complexity of the learn-to-stabilize problem in Linear…
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines
Jueming Hu, Zhe Xu, Weichang Wang +3
In multi-agent reinforcement learning (MARL), it is challenging for a collection of agents to learn complex temporally extended tasks. The difficulties lie in computational complex…
Stability Constrained Reinforcement Learning for Real-Time Voltage Control
Yuanyuan Shi, Guannan Qu, Steven Low +2
Deep reinforcement learning (RL) has been recognized as a promising tool to address the challenges in real-time control of power systems. However, its deployment in real-world powe…
Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems
Yiheng Lin, Yang Hu, Haoyuan Sun +3
We study predictive control in a setting where the dynamics are time-varying and linear, and the costs are time-varying and well-conditioned. At each time step, the controller rece…
Robustness and Consistency in Linear Quadratic Control with Untrusted Predictions
Tongxin Li, Ruixiao Yang, Guannan Qu +4
We study the problem of learning-augmented predictive linear quadratic control. Our goal is to design a controller that balances \textit{"consistency"}, which measures the competit…
Stable Online Control of Linear Time-Varying Systems
Guannan Qu, Yuanyuan Shi, Sahin Lale +2
Linear time-varying (LTV) systems are widely used for modeling real-world dynamical systems due to their generality and simplicity. Providing stability guarantees for LTV systems i…
Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges
Xin Chen, Guannan Qu, Yujie Tang +2
With large-scale integration of renewable generation and distributed energy resources, modern power systems are confronted with new operational challenges, such as growing complexi…
Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks
Andreas Venzke, Guannan Qu, Steven Low +1
This paper introduces for the first time a framework to obtain provable worst-case guarantees for neural network performance, using learning for optimal power flow (OPF) problems a…
Combining Model-Based and Model-Free Methods for Nonlinear Control: A Provably Convergent Policy Gradient Approach
Guannan Qu, Chenkai Yu, Steven Low +1
Model-free learning-based control methods have seen great success recently. However, such methods typically suffer from poor sample complexity and limited convergence guarantees. T…
Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward
Guannan Qu, Yiheng Lin, Adam Wierman +1
It has long been recognized that multi-agent reinforcement learning (MARL) faces significant scalability issues due to the fact that the size of the state and action spaces are exp…
Multi-Agent Reinforcement Learning in Stochastic Networked Systems
Yiheng Lin, Guannan Qu, Longbo Huang +1
We study multi-agent reinforcement learning (MARL) in a stochastic network of agents. The objective is to find localized policies that maximize the (discounted) global reward. In g…
Finite-Time Analysis of Asynchronous Stochastic Approximation and -Learning
Guannan Qu, Adam Wierman
We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence ra…
Scalable Reinforcement Learning for Multi-Agent Networked Systems
Guannan Qu, Adam Wierman, Na Li
We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies suc…
Exploiting Fast Decaying and Locality in Multi-Agent MDP with Tree Dependence Structure
Guannan Qu, Na Li
This paper considers a multi-agent Markov Decision Process (MDP), where there are agents and each agent is associated with a state and action taking values from…
Semi-Global Exponential Stability of Augmented Primal-Dual Gradient Dynamics for Constrained Convex Optimization
Yujie Tang, Guannan Qu, Na Li
Primal-dual gradient dynamics that find saddle points of a Lagrangian have been widely employed for handling constrained optimization problems. Building on existing methods, we ext…
Distributed Optimal Voltage Control with Asynchronous and Delayed Communication
Sindri Magnússon, Guannan Qu, Na Li
The increased penetration of volatile renewable energy into distribution networks necessities more efficient distributed voltage control. In this paper, we design distributed feedb…
Optimal Distributed Feedback Voltage Control under Limited Reactive Power
Guannan Qu, Na Li
In this paper, we propose a distributed voltage control in power distribution networks through reactive power compensation. The proposed control can (i) operate in a distributed fa…
On the Exponential Stability of Primal-Dual Gradient Dynamics
Guannan Qu, Na Li
Continuous time primal-dual gradient dynamics that find a saddle point of a Lagrangian of an optimization problem have been widely used in systems and control. While the global asy…