Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning

Baiyuan Chen; Shinji Ito; Masaaki Imaizumi

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arXiv · 2508.16027

Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning

Baiyuan Chen·Shinji Ito·Masaaki Imaizumi

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Abstract

Transformers have demonstrated exceptional performance across a wide range of domains. While their ability to perform reinforcement learning in-context has been established both theoretically and empirically, their behavior in non-stationary environments remains less understood. In this study, we address this gap by showing that transformers can achieve nearly optimal dynamic regret bounds in non-stationary settings. We prove that transformers are capable of approximating strategies used to handle non-stationary environments and can learn the approximator in the in-context learning setup. Our experiments further show that transformers can match or even outperform existing expert algorithms in such environments.

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Baiyuan Chen, Shinji Ito, Masaaki Imaizumi. 2025-08-22. Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning. https://arxiv.org/abs/2508.16027

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