text: 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.
  @cite_35: Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial intelligence that is able to learn and reason in the same way that humans do. We introduce beta-VAE, a new state-of-the-art framework for automated discovery of interpretable factorised latent representations from raw image data in a completely unsupervised manner. Our approach is a modification of the variational autoencoder (VAE) framework. We introduce an adjustable hyperparameter beta that balances latent channel capacity and independence constraints with reconstruction accuracy. We demonstrate that beta-VAE with appropriately tuned beta > 1 qualitatively outperforms VAE (beta = 1), as well as state of the art unsupervised (InfoGAN) and semi-supervised (DC-IGN) approaches to disentangled factor learning on a variety of datasets (celebA, faces and chairs). Furthermore, we devise a protocol to quantitatively compare the degree of disentanglement learnt by different models, and show that our approach also significantly outperforms all baselines quantitatively. Unlike InfoGAN, beta-VAE is stable to train, makes few assumptions about the data and relies on tuning a single hyperparameter, which can be directly optimised through a hyper parameter search using weakly labelled data or through heuristic visual inspection for purely unsupervised data.
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  @cite_36: 
  @cite_32: 
  @cite_3: The goal of imitation learning is to mimic expert behavior without access to an explicit reward signal. Expert demonstrations provided by humans, however, often show significant variability due to latent factors that are typically not explicitly modeled. In this paper, we propose a new algorithm that can infer the latent structure of expert demonstrations in an unsupervised way. Our method, built on top of Generative Adversarial Imitation Learning, can not only imitate complex behaviors, but also learn interpretable and meaningful representations of complex behavioral data, including visual demonstrations. In the driving domain, we show that a model learned from human demonstrations is able to both accurately reproduce a variety of behaviors and accurately anticipate human actions using raw visual inputs. Compared with various baselines, our method can better capture the latent structure underlying expert demonstrations, often recovering semantically meaningful factors of variation in the data.
  @cite_6: 
  @cite_27: 
  @cite_5: We present a framework for learning disentangled and interpretable jointly continuous and discrete representations in an unsupervised manner. By augmenting the continuous latent distribution of variational autoencoders with a relaxed discrete distribution and controlling the amount of information encoded in each latent unit, we show how continuous and categorical factors of variation can be discovered automatically from data. The learned model also contains an inference network which can infer quantities such as angle and width of objects from image data in a completely unsupervised manner. Our experiments show that the framework disentangles continuous and discrete generative factors on various datasets, including disentangling digit type from stroke thickness, angle and width on MNIST, chair type from azimuth and width on the Chairs dataset and age from azimuth on CelebA.
  @cite_16: We decompose the evidence lower bound to show the existence of a term measuring the total correlation between latent variables. We use this to motivate our @math -TCVAE (Total Correlation Variational Autoencoder), a refinement of the state-of-the-art @math -VAE objective for learning disentangled representations, requiring no additional hyperparameters during training. We further propose a principled classifier-free measure of disentanglement called the mutual information gap (MIG). We perform extensive quantitative and qualitative experiments, in both restricted and non-restricted settings, and show a strong relation between total correlation and disentanglement, when the latent variables model is trained using our framework.
  @cite_13: We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constrained to be locally linear. Our model is derived directly from an optimal control formulation in latent space, supports long-term prediction of image sequences and exhibits strong performance on a variety of complex control problems.
  @cite_17: 
summary: The most studied unsupervised learning-based methods employ generative models, especially VAEs and InfoGANs @cite_36 . VAEs combine Bayes and autoencoder approaches to embed encoded features based on a given probability distribution. Dupont @cite_5 proposed a joint-VAE that disentangled continuous and discrete representations. @cite_35 introduced an adjustable hyperparameter, @math , to a standard VAE to balance latent channel capacity and independence constraints with reconstruction accuracy. Kim and Mnih @cite_17 added a discriminator to @math -VAE to estimate Total Correlation, and @cite_16 decomposed a @math -VAE equation and refined it to improve the disentanglement ability without using additional hyperparameters. InfoGANs attempted to learn a generator such that they cheated the discriminator and maximized the mutual information between synthetic samples and newly introduced latent codes. Inspired by InfoGAN, @cite_3 proposed combining generative adversarial imitation learning @cite_32 with a variational lower bound of the mutual information. On the application side, for example, unsupervised approaches for sequential data @cite_4 @cite_27 @cite_6 , and control and planing @cite_13 @cite_3 were also proposed.