Online Statistical Inference of Constant Sample-averaged Q-Learning

2026-03-31 · Source: stat.ML updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, long

Summary

This paper introduces a framework for statistical online inference in a sample-averaged Q-learning approach, aiming to address high variance and instability in reinforcement learning algorithms, particularly in noisy or sparse reward environments. The authors adapt the functional central limit theorem (FCLT) for their modified algorithm under general conditions and construct confidence intervals for Q-values using a random scaling method. They compare this approach against traditional Q-learning on two problems: a grid world and a dynamic resource-matching problem. While the grid world results showed traditional Q-learning having higher coverage rates, the more complex dynamic resource-matching problem demonstrated that the sample-averaged Q-learning yielded tighter confidence intervals, indicating improved accuracy in confidence measures.

Key takeaway

For research scientists developing robust reinforcement learning algorithms, consider integrating sample-averaged Q-learning with functional central limit theorem (FCLT) and random scaling. This approach can yield more accurate confidence measures and tighter confidence intervals for Q-values, especially in complex, high-dimensional environments like dynamic resource-matching problems, enhancing the reliability and interpretability of your models.

Key insights

Sample-averaged Q-learning with FCLT and random scaling improves statistical inference in RL.

Principles

• FCLT provides theoretical basis for statistical properties of Q-learning.
• Random scaling avoids additional estimation steps and hyperparameters.

Method

The method involves adapting the FCLT for sample-averaged Q-learning, then constructing confidence intervals for Q-values using a random scaling quantity $\widehat{D}_{T}$ and an asymptotically pivotal statistic $\widehat{\kappa}$.

In practice

• Apply sample-averaged Q-learning for dynamic resource-matching.
• Use random scaling for tighter confidence intervals in complex RL tasks.

Topics

• Reinforcement Learning
• Q-learning
• Statistical Inference
• Functional Central Limit Theorem
• Random Scaling

Best for: Research Scientist, AI Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by stat.ML updates on arXiv.org.