137 citations · 656 across the 54 of their papers we have counts for
6 papers · 1 filter
Faster Single-loop Algorithms for Minimax Optimization without Strong Concavity
Junchi Yang, Antonio Orvieto, Aurelien Lucchi +1
Gradient descent ascent (GDA), the simplest single-loop algorithm for nonconvex minimax optimization, is widely used in practical applications such as generative adversarial networ…
Linear Convergence of Entropy-Regularized Natural Policy Gradient with Linear Function Approximation
Semih Cayci, Niao He, R. Srikant
Natural policy gradient (NPG) methods with entropy regularization achieve impressive empirical success in reinforcement learning problems with large state-action spaces. However, t…
The Complexity of Nonconvex-Strongly-Concave Minimax Optimization
Siqi Zhang, Junchi Yang, Cristóbal Guzmán +2
This paper studies the complexity for finding approximate stationary points of nonconvex-strongly-concave (NC-SC) smooth minimax problems, in both general and averaged smooth finit…
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction
Donghwan Lee, Niao He, Seungjae Lee +2
The building sector consumes the largest energy in the world, and there have been considerable research interests in energy consumption and comfort management of buildings. Inspire…
Sample Complexity and Overparameterization Bounds for Temporal Difference Learning with Neural Network Approximation
Semih Cayci, Siddhartha Satpathi, Niao He +1
In this paper, we study the dynamics of temporal difference learning with neural network-based value function approximation over a general state space, namely, \emph{Neural TD lear…
A Discrete-Time Switching System Analysis of Q-learning
Donghwan Lee, Jianghai Hu, Niao He
This paper develops a novel control-theoretic framework to analyze the non-asymptotic convergence of Q-learning. We show that the dynamics of asynchronous Q-learning with a constan…