Disentangled (Un)Controllable Features
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2022
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914627438247936 |
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| author | Kooi, Jacob E. Hoogendoorn, Mark François-Lavet, Vincent |
| author_facet | Kooi, Jacob E. Hoogendoorn, Mark François-Lavet, Vincent |
| contents | In the context of MDPs with high-dimensional states, downstream tasks are predominantly applied on a compressed, low-dimensional representation of the original input space. A variety of learning objectives have therefore been used to attain useful representations. However, these representations usually lack interpretability of the different features. We present a novel approach that is able to disentangle latent features into a controllable and an uncontrollable partition. We illustrate that the resulting partitioned representations are easily interpretable on three types of environments and show that, in a distribution of procedurally generated maze environments, it is feasible to interpretably employ a planning algorithm in the isolated controllable latent partition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2211_00086 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Disentangled (Un)Controllable Features Kooi, Jacob E. Hoogendoorn, Mark François-Lavet, Vincent Machine Learning Artificial Intelligence In the context of MDPs with high-dimensional states, downstream tasks are predominantly applied on a compressed, low-dimensional representation of the original input space. A variety of learning objectives have therefore been used to attain useful representations. However, these representations usually lack interpretability of the different features. We present a novel approach that is able to disentangle latent features into a controllable and an uncontrollable partition. We illustrate that the resulting partitioned representations are easily interpretable on three types of environments and show that, in a distribution of procedurally generated maze environments, it is feasible to interpretably employ a planning algorithm in the isolated controllable latent partition. |
| title | Disentangled (Un)Controllable Features |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2211.00086 |