arXiv AI June 3, 2026 By Donghwan Lee

Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics

arXiv:2606. 02645v1 Announce Type: cross Abstract: Periodic target updates in Q-learning and soft target updates in actor-critic methods are empirically well established stabilization mechanisms, but their precise theoretical explanation is still incomplete.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

reinforcement-learning

arXiv AI

Jun 10

Geometrically Averaged Hard Target Updates for Linear Q-Learning

arXiv:2606. 10835v1 Announce Type: cross Abstract: Periodic hard target updates are among the most common stabilization devices in modern deep Q-learning.

By Donghwan Lee

reinforcement-learning

arXiv Machine Learning

Jul 7

A Switching System Theory of Q-Learning with Linear Function Approximation

arXiv:2605. 11021v3 Announce Type: replace Abstract: Q-learning is a fundamental algorithmic primitive in reinforcement learning.

By Donghwan Lee, Han-Dong Lim

reinforcement-learning

arXiv AI

Aug 11

Switching Theory for Q-Learning

arXiv:2604. 19569v5 Announce Type: replace-cross Abstract: Q-learning is a fundamental algorithmic primitive in reinforcement learning.

By Donghwan Lee

reinforcement-learning

arXiv AI

Jul 1

Lyapunov-Certified Direct Switching Theory for Q-Learning

arXiv:2604. 19569v4 Announce Type: replace-cross Abstract: Q-learning is a fundamental algorithmic primitive in reinforcement learning.

By Donghwan Lee

reinforcement-learning

arXiv AI

Jun 26

Heavy-Ball Q-Learning with Residual Weighting Correction

arXiv:2606. 27112v1 Announce Type: cross Abstract: This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence.

By Donghwan Lee

reinforcement-learning

Hugging Face Trending Papers

Jun 25

Heavy-Ball Q-Learning with Residual Weighting Correction

This paper proposes a corrected heavy-ball Q-learning method for reinforcement learning (RL) and establishes its convergence. It also identifies conditions under which the method is theoretically guaranteed to converge faster than standard Q-learning.

reinforcement-learning

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