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cs.LG2026
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…
cs.LG2026
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…
cs.LG2026
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…