## 1 Introduction  
Artificial Intelligence seeks to answer the question of how an agent can perceive, understand, predict and manipulate an environment it is placed in [1]. An autonomous or mapless navigation system, which implicitly performs localization and mapping, equips the mobile robot to determine its actual position within the reference frame environment and autonomously move to the desired target position [2]. However, the traditional navigation approach consists of algorithms that include simultaneous localization and mapping (SLAM), path planning and motion control, as highlighted in References [2, 3]. Moreover, these methods rely on high-precision global maps or positioning systems, such as GPS, with high limitations in terms of visual capability in unknown environments. Research in the application of mapless navigation to many dynamic real-world scenarios has attracted attention from the research community [4], with more diverse approaches to finding robust alternative solutions being proposed. Machine learning is a sub-field of artificial intelligence (AI) and is the study of theories and algorithms that mimic how the brain learns. Machine learning (ML) allows machines to mimic intelligence without explicit rules defining their behavior [5]. At its core, machine learning is a function approximation. The field of ML is vast and ever growing but can be broadly divided into three sections, namely:* Supervised learning methods are concerned with inferring a function from input-output pairs. The learning exhibited by these methods is achieved by optimizing a loss function computed from the output produced from the model and the expected output or ground truth. * Unsupervised learning techniques attempt to learn the hidden structure present in training data without the use of an explicit error/reward signal. These techniques attempt to learn the structure naturally present in the training set and achieve this without an error or reward signal. * Reinforcement learning (RL) attempts to learn a policy, that maps percepts to actions by interaction with the environment. The RL algorithms are inspired by behavioral psychology and attempt to answer the question of how an agent placed in an environment can learn to behave optimally. For a machine learning algorithm to generalize beyond the data it was trained on, the number of data samples used for training must be relatively large. Few-shot learning aims at learning models in environments where large datasets are not readily available because of privacy, safety or ethical concerns [6]. Supervised agents receive training information in the form \([\text{x},\,y]\), where \(x\) denotes a vector of features that describes the problem being solved by the agent. The variable \(y\) denotes the ground truth for the observed values in \(x\). Similarly, the following also applies:  
* For an agent tasked with classifying images, the vector \(x\) consists of pixel intensities. The ground truth is a discrete value representing the class that the image belongs to. * An agent classifying bank transactions as fraudulent or not, uses data related to transactions to form \(x\). The ground truth \(y\) for each \(x\) is either a 1, meaning that the transaction data in \(x\) represents a fraudulent transaction or a 0, meaning that \(x\) represents a valid transaction. * A robot navigating in an environment makes use of various sensor readings to form \(x\). In the case of the robot navigating, defining a ground-truth becomes challenging. An agents success or failure rate at a given task is highly related to how well the task is described using the feature vector. When the task to be performed by the agent is not governed by a set of rules or in cases when the construction of such rules are infeasible, methods are required that learn a function mapping \(x\) to \(y\) through interaction with the environment.