On the Identifiability of Latent Action Policies
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866912622343880704 |
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| author | Lachapelle, Sébastien |
| author_facet | Lachapelle, Sébastien |
| contents | We study the identifiability of latent action policy learning (LAPO), a framework introduced recently to discover representations of actions from video data. We formally describe desiderata for such representations, their statistical benefits and potential sources of unidentifiability. Finally, we prove that an entropy-regularized LAPO objective identifies action representations satisfying our desiderata, under suitable conditions. Our analysis provides an explanation for why discrete action representations perform well in practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01337 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Identifiability of Latent Action Policies Lachapelle, Sébastien Machine Learning We study the identifiability of latent action policy learning (LAPO), a framework introduced recently to discover representations of actions from video data. We formally describe desiderata for such representations, their statistical benefits and potential sources of unidentifiability. Finally, we prove that an entropy-regularized LAPO objective identifies action representations satisfying our desiderata, under suitable conditions. Our analysis provides an explanation for why discrete action representations perform well in practice. |
| title | On the Identifiability of Latent Action Policies |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.01337 |