564 citations · 1.6k across the 27 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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…