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arXiv · 2511.11973

Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression

Xinming Gao·Shangzhe Li·Yujin Cai·Wenwu Yu
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Abstract

Offline reinforcement learning (RL) enables policy learning from fixed datasets without further environment interaction, making it particularly valuable in high-risk or costly domains. Extreme $Q$-Learning (XQL) is a recent offline RL method that models Bellman errors using the Extreme Value Theorem, yielding strong empirical performance. However, XQL and its stabilized variant MXQL suffer from notable limitations: both require extensive hyperparameter tuning specific to each dataset and domain, and also exhibit instability during training. To address these issues, we proposed a principled method to estimate the temperature coefficient $β$ via quantile regression under mild assumptions. To further improve training stability, we introduce a value regularization technique with mild generalization, inspired by recent advances in constrained value learning. Experimental results demonstrate that the proposed algorithm achieves competitive or superior performance across a range of benchmark tasks, including D4RL and NeoRL2, while maintaining stable training dynamics and using a consistent set of hyperparameters across all datasets and domains.

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Xinming Gao, Shangzhe Li, Yujin Cai, Wenwu Yu. 2026-04-14. Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression. https://arxiv.org/abs/2511.11973

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