### Related Work  
As outlined in the introduction, the topic of this paper is learning in a reduced space, addressing the issue of how to obtain a suitable action database. It has been shown that learning of robot actions can be more effective in a smaller search space [15]. Therefore, different dimensionality reduction techniques were used to reduce the search space, for example, PCA, discriminant function analysis--such as linear discriminant analysis (LDA), kernel PCA, and AE. The first two are linear in nature, while kernel PCA and AE are not. These methods were applied in various robotic applications. As an example, PCA was used to reduce the amount of training data for learning the behavior of an articulated body in [20] and to obtain optimal robot motion in real-time [21]. These two works both say that obtaining a suitable database and working with high amounts of data are among the key problems. LDA, on the other hand, is typically applied for classification, but not necessarily better than PCA [22]. Linear approximation of real-world actions might not be the best for the nonlinear world. It has been shown that AEs work much better than PCA to reduce the dimensionality of the data [17]. Similar results were reported in [18], where generalization of throwing actions in either PCA latent space or AE latent space were compared, with the database acquired in simulation. Kernel PCA [23] can provide for a nonlinear reduction of dimensionality of data. Among the issues that need to be addressed to implement kernel PCA are the storage of a large kernel matrix and the selection of the nonlinear kernel. The utilization of different kernels was investigated for modeling of humanoid robot motions in [24]. Given that we are dealing with a nonlinear task in a nonlinear world, we chose autoencoders for dimensionality reduction. All dimensionality reduction techniques require a database. It is often not feasible to record thousands of robot actions that are needed to train neural networks. To illustrate, in order to learn proper grasping techniques, close to a million of repetitions were carried out with a cluster of robots [25]. The question of database acquisition was tackled also in other settings, e.g., for statistical generalization that typically requires less data than neural network training. In [26], the authors investigated how many example executions and in which order they need to be added to enable meaningful generalization. [PERSON] et al. [18] studied how many database entries are required for generalization of real-world actions using a simulated database. Other instances of database learning for generalization include the work in [27], where the authors autonomously added database entries after generalization and adaptation. Similar ideas were investigated in [28; 29]. In this paper, we use a different approach. We first record a small number or real-world actions and generalize to a larger set. We use this larger set to train autoencoder, and then implement learning methodologies in its latent space. Action representations and Reinforcement Learning (RL) are discussed in, for example, [5; 18; 30; 31]. Furthermore, action generalization in feature space of a quadruped robot is discussed in [32]. RL and especially Deep Reinforcement Learning (DRL) achieved great results in simulated environments [33; 34]. Nevertheless, all these achievements depend on the ability to collect large amounts of accurate data. For the robots that are equipped only with noisy sensors data and are supposed to work in everyday and ever-changing environments, the application of such learning algorithms might be difficult. A promising method is to train control policies in simulated environments, where data generation is safe and convenient, and then transfer the learned policies to the real world [35]. This process is called transfer learning (TL).