Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918166270050304 |
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| author | Chen, Baiyuan Ito, Shinji Imaizumi, Masaaki |
| author_facet | Chen, Baiyuan Ito, Shinji Imaizumi, Masaaki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_16027 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning Chen, Baiyuan Ito, Shinji Imaizumi, Masaaki Machine Learning 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. |
| title | Optimal Dynamic Regret by Transformers for Non-Stationary Reinforcement Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.16027 |