Use of sample-splitting and cross-fitting techniques to mitigate the risks of double-dipping in behaviour-agnostic reinforcement learning
Comparative Analysis
Y. Aslan (TU Delft - Electrical Engineering, Mathematics and Computer Science)
S.R. Bongers – Mentor (TU Delft - Sequential Decision Making)
FA Oliehoek – Mentor (TU Delft - Sequential Decision Making)
C.M. Jonker – Graduation committee member (TU Delft - Interactive Intelligence)
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Abstract
This paper addresses the issue of double-dipping in off-policy evaluation (OPE) in behaviour-agnostic reinforcement learning, where the same dataset is used for both training and estimation, leading to overfitting and inflated performance metrics especially for variance. We introduce SplitDICE, which incorporates sample-splitting and cross-fitting techniques to mitigate double-dipping effects in the DICE family of estimators. Focusing specifically on 2-fold and 5-fold cross-fitting strategies, the original off-policy dataset is partitioned with random-split to get separate training and evaluation datasets. Experimental results demonstrate that SplitDICE, particularly with 5-fold cross-fitting, significantly reduces error, bias, and variance compared to naive DICE implementations, providing a more doubly-robust solution for behavior-agnostic OPE.