Online Statistical Inference of Constant Sample-averaged Q-Learning
Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, particularly in environments with noise or sparse rewards. In this paper, we propose a framework to perform statistical online inference for a sample-averaged Q-learning approach. We adapt the functional central limit theorem (FCLT) for the modified algorithm under some general conditions and then construct confidence intervals for the Q-values via random scaling. We conduct experiments to perform inference on both the modified approach and its traditional counterpart, Q-learning using random sc...
| Authors | Saunak Kumar Panda, Tong Li, Ruiqi Liu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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