1 citations · 1 across the 13 of their papers we have counts for
13 papers
A Switching System Theory of Q-Learning with Linear Function Approximation
Donghwan Lee, Han-Dong Lim
Q-learning is a fundamental algorithmic primitive in reinforcement learning. This paper develops a new framework for analyzing linear Q-learning from a switching linear system (SLS…
Contraction-Aligned Analysis of Soft Bellman Residual Minimization with Weighted Lp-Norm for Markov Decision Problem
Hyukjun Yang, Han-Dong Lim, Donghwan Lee
The problem of solving Markov decision processes under function approximation remains a fundamental challenge, even under linear function approximation settings. A key difficulty a…
Periodic Regularized Q-Learning
Hyukjun Yang, Han-Dong Lim, Donghwan Lee
In reinforcement learning (RL), Q-learning is a fundamental algorithm whose convergence is guaranteed in the tabular setting. However, this convergence guarantee does not hold unde…
Understanding the theoretical properties of projected Bellman equation, linear Q-learning, and approximate value iteration
Han-Dong Lim, Donghwan Lee
In this paper, we study the theoretical properties of the projected Bellman equation (PBE) and two algorithms to solve this equation: linear Q-learning and approximate value iterat…
Analysis of Off-Policy -Step TD-Learning with Linear Function Approximation
Han-Dong Lim, Donghwan Lee
This paper analyzes multi-step temporal difference (TD)-learning algorithms within the ``deadly triad'' scenario, characterized by linear function approximation, off-policy learnin…
A finite time analysis of distributed Q-learning
Han-Dong Lim, Donghwan Lee
Multi-agent reinforcement learning (MARL) has witnessed a remarkable surge in interest, fueled by the empirical success achieved in applications of single-agent reinforcement learn…
Learning the Model While Learning Q: Finite-Time Sample Complexity of Online SyncMBQ
Han-Dong Lim, HyeAnn Lee, Donghwan Lee
Reinforcement learning has witnessed significant advancements, particularly with the emergence of model-based approaches. Among these, -learning has proven to be a powerful algo…
A primal-dual perspective for distributed TD-learning
Han-Dong Lim, Donghwan Lee
The goal of this paper is to investigate distributed temporal difference (TD) learning for a networked multi-agent Markov decision process. The proposed approach is based on distri…
Finite-Time Analysis of Temporal Difference Learning with Experience Replay
Han-Dong Lim, Donghwan Lee
Temporal-difference (TD) learning is widely regarded as one of the most popular algorithms in reinforcement learning (RL). Despite its widespread use, it has only been recently tha…
Backstepping Temporal Difference Learning
Han-Dong Lim, Donghwan Lee
Off-policy learning ability is an important feature of reinforcement learning (RL) for practical applications. However, even one of the most elementary RL algorithms, temporal-diff…
Finite-Time Analysis of Asynchronous Q-learning under Diminishing Step-Size from Control-Theoretic View
Han-Dong Lim, Donghwan Lee
Q-learning has long been one of the most popular reinforcement learning algorithms, and theoretical analysis of Q-learning has been an active research topic for decades. Although r…
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…
New Versions of Gradient Temporal Difference Learning
Donghwan Lee, Han-Dong Lim, Jihoon Park +1
Sutton, Szepesvári and Maei introduced the first gradient temporal-difference (GTD) learning algorithms compatible with both linear function approximation and off-policy training.…