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