Periodic Regularized Q-Learning

• URL: http://arxiv.org/abs/2602.03301v1
• Date: Tue, 03 Feb 2026 09:28:06 GMT
• Title: Periodic Regularized Q-Learning
• Authors: Hyukjun Yang, Han-Dong Lim, Donghwan Lee,
• Abstract summary: We propose a new algorithm, periodic regularized Q-learning (PRQ)<n>We provide a rigorous theoretical analysis that proves finite-time convergence guarantees for PRQ under linear function approximation.
• Score: 9.333190920811626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 under linear function approximation. To overcome this limitation, a significant line of research has introduced regularization techniques to ensure stable convergence under function approximation. In this work, we propose a new algorithm, periodic regularized Q-learning (PRQ). We first introduce regularization at the level of the projection operator and explicitly construct a regularized projected value iteration (RP-VI), subsequently extending it to a sample-based RL algorithm. By appropriately regularizing the projection operator, the resulting projected value iteration becomes a contraction. By extending this regularized projection into the stochastic setting, we establish the PRQ algorithm and provide a rigorous theoretical analysis that proves finite-time convergence guarantees for PRQ under linear function approximation.

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