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Symmetry-Based Disentangled Representation Learning requires Interaction with Environments

Caselles-Dupré, H., Garcia Ortiz, M. ORCID: 0000-0003-4729-7457 and Filliat, D. (2019). Symmetry-Based Disentangled Representation Learning requires Interaction with Environments. Paper presented at the Workshop on Structure & Priors in Reinforcement Learning - ICLR 2019, 6 May 2019, New Orleans, USA.


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 fixed data samples. Agents should interact with the environment to discover its symmetries. All of our experiments can be reproduced on Colab:

Publication Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Mathematics, Computer Science & Engineering > Computer Science
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