5 papers
Finite-Time Accuracy of Temporal-Difference Learning Under Schur-Stable Recursions
Donghwan Lee, Do Wan Kim
Temporal difference (TD) learning is a cornerstone reinforcement learning (RL) method for policy evaluation, where the goal is to estimate the value function of a Markov decision p…
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…
Relaxed Conditions for Parameterized Linear Matrix Inequality in the Form of Double Sum
Do Wan Kim, Dong Hwan Lee
The aim of this study is to investigate less conservative conditions for a parameterized linear matrix inequality (PLMI) expressed in the form of a double convex sum. This type of…
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…