Abstract
In the context of MDPs with high-dimensional states, reinforcement learning can achieve better results when using a compressed, low-dimensional representation of the original input space. A variety of learning objectives have therefore been used to learn useful representations. However, these representations usually lack interpretability of the different features. We propose a representation learning algorithm that is able to disentangle latent features into a controllable and an uncontrollable part. The resulting representations are easily interpretable and can be used for learning and planning efficiently by leveraging the specific properties of the two parts. To highlight the benefits of the approach, the disentangling properties of the algorithm are illustrated in three different environments.
Abstract (translated)
URL
https://arxiv.org/abs/2211.00086
