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.
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.
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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.
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.
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.
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.
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.
arXiv AI
Jul 10
Spectral Analysis of Dueling Q-Learning
arXiv:2607. 08340v1 Announce Type: cross Abstract: Q-learning is a fundamental algorithm in reinforcement learning (RL) for solving discounted Markov decision processes (MDPs) when the transition kernel is unknown.
arXiv AI
Jul 7
Sign-Separated Asymmetric Finite-Time Error Analysis of Q-Learning
arXiv:2605. 16103v2 Announce Type: replace Abstract: Q-learning is known to suffer from overestimation bias: because the Bellman update maximizes noisy or imperfect action-value estimates, positive errors can be selected and propagated, causing learned values to exceed the true optimal values.
arXiv AI
Jul 17
Reinforcement Learning in Switching Non-Stationary Markov Decision Processes: Algorithms and Convergence Analysis
arXiv:2503. 18607v2 Announce Type: replace-cross Abstract: We introduce the Switching Non-Stationary Markov Decision Process (SNS-MDP) framework, in which the environment transitions among a finite set of MDPs governed by a latent Markov chain while the agent observes only the external state.
arXiv AI
Jun 16
Deep Q-Learning on H\"older Spaces
arXiv:2606. 16846v1 Announce Type: cross Abstract: We study the operator-theoretic core of Q-learning in continuous-time stochastic control with continuous states and actions.
arXiv Machine Learning
Jun 16
Finite-Time Convergence of Distributionally Robust Q-Learning with Linear Function Approximation
arXiv:2510. 01721v3 Announce Type: replace Abstract: Distributionally robust reinforcement learning (DRRL) seeks policies that perform well when the deployment transition model differs from the nominal model generating the data.
arXiv Machine Learning
Sep 4
Finite-Time Convergence of Single-Trajectory Chi-Square Robust Q-Learning With Linear Function Approximation
The paper investigates model‑free robust Q‑learning with χ² uncertainty sets and linear function approximation, using data from a single trajectory of an unknown nominal MDP. It introduces a variational reformulation of the robust Bellman target and a blockwise frozen‑target scheme to overcome estimation and non‑contractivity challenges, and proves a finite‑time error bound for every discount factor γ in (0,1). A neural‑network experiment demonstrates the practical use of the variational target in a continuous‑state nonlinear‑control task.