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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.18614 |
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| _version_ | 1866911019084808192 |
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| author | Weinberger, Simón Cugliari, Jairo |
| author_facet | Weinberger, Simón Cugliari, Jairo |
| contents | In reinforcement learning, the softmax parametrization is the standard approach for policies over discrete action spaces. However, it fails to capture the order relationship between actions. Motivated by a real-world industrial problem, we propose a novel policy parametrization based on ordinal regression models adapted to the reinforcement learning setting. Our approach addresses practical challenges, and numerical experiments demonstrate its effectiveness in real applications and in continuous action tasks, where discretizing the action space and applying the ordinal policy yields competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18614 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Policy gradient methods for ordinal policies Weinberger, Simón Cugliari, Jairo Machine Learning In reinforcement learning, the softmax parametrization is the standard approach for policies over discrete action spaces. However, it fails to capture the order relationship between actions. Motivated by a real-world industrial problem, we propose a novel policy parametrization based on ordinal regression models adapted to the reinforcement learning setting. Our approach addresses practical challenges, and numerical experiments demonstrate its effectiveness in real applications and in continuous action tasks, where discretizing the action space and applying the ordinal policy yields competitive performance. |
| title | Policy gradient methods for ordinal policies |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.18614 |