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arXiv · 2305.18388

The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation

Mark Rowland·Yunhao Tang·Clare Lyle·Rémi Munos·Marc G. Bellemare·Will Dabney
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Abstract

We study the problem of temporal-difference-based policy evaluation in reinforcement learning. In particular, we analyse the use of a distributional reinforcement learning algorithm, quantile temporal-difference learning (QTD), for this task. We reach the surprising conclusion that even if a practitioner has no interest in the return distribution beyond the mean, QTD (which learns predictions about the full distribution of returns) may offer performance superior to approaches such as classical TD learning, which predict only the mean return, even in the tabular setting.

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Mark Rowland, Yunhao Tang, Clare Lyle, Rémi Munos, Marc G. Bellemare, Will Dabney. 2023-05-28. The Statistical Benefits of Quantile Temporal-Difference Learning for Value Estimation. https://arxiv.org/abs/2305.18388

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