Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
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We introduce a method for disentangling independently controllable and uncontrollable factors of variation by interacting with the world. Disentanglement leads to good representations and it is important when applying deep neural networks (DNNs) in fields where explanations are necessary. This article focuses on reinforcement learning (RL) approach for disentangling factors of variation, however, previous methods lacks a mechanism for representing uncontrollable obstacles. To tackle this problem, we train two DNNs simultaneously: one that represents the controllable object and another that represents the uncontrollable obstacles. During training, we used the parameters from a previous RL-based model as our initial parameters to improve stability. We also conduct simple toy simulations to show that our model can indeed disentangle controllable and uncontrollable factors of variation and that it is effective for a task involving the acquisition of extrinsic rewards.
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