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It has been postulated that a good representation is one that disentanglesthe underlying explanatory factors of variation. However, it remains an openquestion what kind of training framework could potentially achieve that.Whereas most previous work focuses on the static setting (e.g., with images),we postulate that some of the causal factors could be discovered if the learneris allowed to interact with its environment. The agent can experiment withdifferent actions and observe their effects. More specifically, we hypothesizethat some of these factors correspond to aspects of the environment which areindependently controllable, i.e., that there exists a policy and a learnablefeature for each such aspect of the environment, such that this policy canyield changes in that feature with minimal changes to other features thatexplain the statistical variations in the observed data. We propose a specificobjective function to find such factors, and verify experimentally that it canindeed disentangle independently controllable aspects of the environmentwithout any extrinsic reward signal.

Disentangling the independently controllable factors of variation by interacting with the world