59 citations · 85 across the 15 of their papers we have counts for
15 papers
A Bandit Learning Method for Continuous Games under Feedback Delays with Residual Pseudo-Gradient Estimate
Yuanhanqing Huang, Jianghai Hu
Learning in multi-player games can model a large variety of practical scenarios, where each player seeks to optimize its own local objective function, which at the same time relies…
Bandit Online Learning in Merely Coherent Games with Multi-Point Pseudo-Gradient Estimate
Yuanhanqing Huang, Jianghai Hu
Non-cooperative games serve as a powerful framework for capturing the interactions among self-interested players and have broad applicability in modeling a wide range of practical…
Zeroth-Order Learning in Continuous Games via Residual Pseudogradient Estimates
Yuanhanqing Huang, Jianghai Hu
A variety of practical problems can be modeled by the decision-making process in multi-player games where a group of self-interested players aim at optimizing their own local objec…
On the Convergence Rates of A Nash Equilibrium Seeking Algorithm in Potential Games with Information Delays
Yuanhanqing Huang, Jianghai Hu
This paper investigates the equilibrium convergence properties of a proposed algorithm for potential games with continuous strategy spaces in the presence of feedback delays, a mai…
Distributed Stochastic Nash Equilibrium Learning in Locally Coupled Network Games with Unknown Parameters
Yuanhanqing Huang, Jianghai Hu
In stochastic Nash equilibrium problems (SNEPs), it is natural for players to be uncertain about their complex environments and have multi-dimensional unknown parameters in their m…
A Primal Decomposition Approach to Globally Coupled Aggregative Optimization over Networks
Yuanhanqing Huang, Jianghai Hu
We consider a class of multi-agent optimization problems, where each agent has a local objective function that depends on its own decision variables and the aggregate of others, an…
Distributed Computation of Stochastic GNE with Partial Information: An Augmented Best-Response Approach
Yuanhanqing Huang, Jianghai Hu
In this paper, we focus on the stochastic generalized Nash equilibrium problem (SGNEP) which is an important and widely-used model in many different fields. In this model, subject…
A Distributed GNE Seeking Algorithm Using the Douglas-Rachford Splitting Method
Yuanhanqing Huang, Jianghai Hu
We consider a generalized Nash equilibrium problem (GNEP) for a network of players. Each player tries to minimize a local objective function subject to some resource constraints wh…
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…
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…
Column Partition based Distributed Algorithms for Coupled Convex Sparse Optimization: Dual and Exact Regularization Approaches
Jinglai Shen, Jianghai Hu, Eswar Kumar Hathibelagal Kammara
This paper develops column partition based distributed schemes for a class of large-scale convex sparse optimization problems, e.g., basis pursuit (BP), LASSO, basis pursuit denosi…
Supplemental Material For "Primal-Dual Q-Learning Framework for LQR Design"
Donghwan Lee, Jianghai Hu
Recently, reinforcement learning (RL) is receiving more and more attentions due to its successful demonstrations outperforming human performance in certain challenging tasks. In ou…
Primal-Dual Distributed Temporal Difference Learning
Donghwan Lee, Jianghai Hu
The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for a class of multi-agent Markov decision processes (MDPs). T…
Dynamic Programming for POMDP with Jointly Discrete and Continuous State-Spaces
Donghwan Lee, Niao He, Jianghai Hu
In this work, we study dynamic programming (DP) algorithms for partially observable Markov decision processes with jointly continuous and discrete state-spaces. We consider a class…
On the Optimal Solutions of the Infinite-Horizon Linear Sensor Scheduling Problem
Lin Zhao, Wei Zhang, Jianghai Hu +2
This paper studies the infinite-horizon sensor scheduling problem for linear Gaussian processes with linear measurement functions. Several important properties of the optimal infin…