Distributional Off-Policy Evaluation with Deep Quantile Process Regression
This paper investigates the off-policy evaluation (OPE) problem from a distributional perspective. Rather than focusing solely on the expectation of the total return, as in most existing OPE methods, we aim to estimate the entire return distribution. To this end, we introduce a quantile-based approach for OPE using deep quantile process regression, presenting a novel algorithm called Deep Quantile Process regression-based Off-Policy Evaluation (DQPOPE). We provide new theoretical insights into the deep quantile process regression technique, extending existing approaches that estimate discrete quantiles to estimate a continuous quantile function. A key contribution of our work is the rigor...
| Authors | Qi Kuang, Chao Wang, Yuling Jiao et al. |
|---|---|
| Primary category | stat.ML |
| Published | 20 April 2026 |
| Last updated at source | 24 April 2026 |
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