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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…
cs.LG2025
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…
cs.LG2023
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…