Online Inference for Quantile Temporal Difference Learning in Distributional Reinforcement Learning
In this paper, we study how to perform statistical inference for quantile temporal difference learning (QTD) in distributional reinforcement learning. Assuming access to a generative model, we first establish functional central limit theorems for both synchronous and asynchronous QTD, which show that the averaged iterates of QTD converge weakly to a rescaled Brownian motion. We next provide online inference methods. Based on random scaling, the inference procedure constructs an asymptotically pivotal statistic for inference by using the information along the whole QTD path. Meanwhile, the proposed statistic can be computed online without storing the entire trajectory of QTD iterates. This...
| Authors | Zijie Cheng, Yang Peng, Zhihua Zhang |
|---|---|
| Primary category | stat.ML |
| Published | 13 August 2026 |
| Last updated at source | 14 August 2026 |
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