Independently Controllable Features

Finding features that disentangle the different causes of variation in real data is a difficult task, that has nonetheless received considerable attention in static domains like natural images. Interactive en- vironments, in which an agent can deliberately take actions, offer an opportunity to tackle this task better, because the agent can experiment with different actions and observe their effects. We introduce the idea that in interactive environments, latent factors that control the variation in observed data can be identified by figuring out what the agent can control. We propose a naive method to find factors that explain or measure the effect of the actions of a learner, and test it in illustrative experiments.

Authors

• Emmanuel Bengio
• Valentin Thomas
• Joelle Pineau
• Doina Precup Mila – Quebec Artificial Intelligence Institute & McGill University
• Yoshua Bengio Mila University of Montreal

Keywords

No keywords are indexed for this paper.