14 citations · 22 across the 14 of their papers we have counts for
14 papers
On Some Geometric Behavior of Value Iteration on the Orthant: Switching System Perspective
Donghwan Lee
In this paper, the primary goal is to offer additional insights into the value iteration through the lens of switching system models in the control community. These models establis…
Regularized Q-learning
Han-Dong Lim, Donghwan Lee
Q-learning is widely used algorithm in reinforcement learning community. Under the lookup table setting, its convergence is well established. However, its behavior is known to be u…
On the Semidefinite Duality of Finite-Horizon LQG Problem
Donghwan Lee
In this paper, our goal is to study fundamental foundations of linear quadratic Gaussian (LQG) control problems for stochastic linear time-invariant systems via Lagrangian duality…
Lossless Convexification and Duality
Donghwan Lee
The main goal of this paper is to investigate strong duality of non-convex semidefinite programming problems (SDPs). In the optimization community, it is well-known that a convex o…
Convergence of Dynamic Programming on the Semidefinite Cone
Donghwan Lee
The goal of this paper is to investigate new and simple convergence analysis of dynamic programming for linear quadratic regulator problem of discrete-time linear time-invariant sy…
Data-Driven Control Design with LMIs and Dynamic Programming
Donghwan Lee, Do Wan Kim
The goal of this paper is to develop data-driven control design and evaluation strategies based on linear matrix inequalities (LMIs) and dynamic programming. We consider determinis…
Multi-Objective LQG Design with Primal-Dual Method
Donghwan Lee, Do Wan Kim
The goal of this paper is to study a multi-objective linear quadratic Gaussian (LQG) control problem. In particular, we consider an optimal control problem minimizing a quadratic c…
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…
Target-Based Temporal Difference Learning
Donghwan Lee, Niao He
The use of target networks has been a popular and key component of recent deep Q-learning algorithms for reinforcement learning, yet little is known from the theory side. In this w…
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems
Hyung-Jin Yoon, Huaiyu Chen, Kehan Long +4
We present a machine learning framework for multi-agent systems to learn both the optimal policy for maximizing the rewards and the encoding of the high dimensional visual observat…
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…
Hidden Markov Model Estimation-Based Q-learning for Partially Observable Markov Decision Process
Hyung-Jin Yoon, Donghwan Lee, Naira Hovakimyan
The objective is to study an on-line Hidden Markov model (HMM) estimation-based Q-learning algorithm for partially observable Markov decision process (POMDP) on finite state and ac…
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…
Primal-Dual Algorithm for Distributed Reinforcement Learning: Distributed GTD
Donghwan Lee, Hyungjin Yoon, Naira Hovakimyan
The goal of this paper is to study a distributed version of the gradient temporal-difference (GTD) learning algorithm for multi-agent Markov decision processes (MDPs). The temporal…