Symmetry-Based Disentangled Representation Learning Requires Interaction with Environments
Abstract
Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. Higgins et al. recently proposed Symmetry-Based Disentangled Representation Learning, a definition based on a characterization of symmetries in the environment using group theory. We build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries. Our experiments can be reproduced in Colab and the code is available on GitHub.
Cite
Text
Caselles-Dupré et al. "Symmetry-Based Disentangled Representation Learning Requires Interaction with Environments." Neural Information Processing Systems, 2019.Markdown
[Caselles-Dupré et al. "Symmetry-Based Disentangled Representation Learning Requires Interaction with Environments." Neural Information Processing Systems, 2019.](https://mlanthology.org/neurips/2019/casellesdupre2019neurips-symmetrybased/)BibTeX
@inproceedings{casellesdupre2019neurips-symmetrybased,
title = {{Symmetry-Based Disentangled Representation Learning Requires Interaction with Environments}},
author = {Caselles-Dupré, Hugo and Ortiz, Michael Garcia and Filliat, David},
booktitle = {Neural Information Processing Systems},
year = {2019},
pages = {4606-4615},
url = {https://mlanthology.org/neurips/2019/casellesdupre2019neurips-symmetrybased/}
}