mlsanityData to 14 Oct 2026

The Role of Target Update Frequencies in Q-Learning

arXiv 2602.03911 ↗First posted 3 Feb 2026cs.LG math.OC stat.MLcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

The target network update frequency (TUF) is a central stabilization mechanism in (deep) Q-learning. However, their selection remains poorly understood and is often treated merely as another tunable hyperparameter rather than as a principled design decision. This work provides a theoretical analysis of target fixing in tabular Q-learning through the lens of approximate dynamic programming. We formulate periodic target updates as a nested optimization scheme in which each outer iteration applies an inexact Bellman optimality operator, approximated by a generic inner loop optimizer. Rigorous theory yields a finite-time convergence analysis for the asynchronous sampling setting, specializing to stochastic gradient descent in the inner loop. Our results deliver an explicit characterization of the bias-variance trade-off induced by the target update period, showing how to optimally set this critical hyperparameter. We prove that constant target update schedules are suboptimal, incurring a logarithmic overhead in sample complexity that is entirely avoidable with adaptive schedules. Our analysis shows that the optimal target update frequency increases geometrically over the course of the learning process.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 13 of its 32 references that are in the map. First posted on arXiv 3 Feb 2026.

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13 of 32 references.

References · 13

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  12. 2022Multilevel Optimization for Inverse Problemscited by 3
1 more
  1. 2025Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularizationcited by 3

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  1. 2026Geometrically Averaged Hard Target Updates for Linear Q-Learning
  2. 2026Target Updates May Stabilize Linear Q-Learning: Periodic and Soft Dynamics
  3. 2025Deep Q-Learning with Gradient Target Tracking
  4. 2026Revisiting TD Target Aggregation under Uncertainty in Q-Learning
  5. 2025Bridging the Performance Gap Between Target-Free and Target-Based Reinforcement Learning
  6. 2025From Set Convergence to Pointwise Convergence: Finite-Time Guarantees for Average-Reward Q-Learning with Adaptive Stepsizes
  7. 2026Quantized Evolution Strategies: High-precision Fine-tuning of Quantized LLMs at Low-precision Cost
  8. 2026TabQL: In-Context Q-Learning with Tabular Foundation Models