## 2 Related work
To solve the problem of partial observability, the agent makes use of a long short-term memory (LSTM) cell and demonstrate that this out-performs stacking of recent frames. The authors used 20,000 images from the SUNCG dataset to pre-train their network over the course of two days. Similarly, the authors in [25] proposed the implementation of a few-shot adaptation of visual navigation skills to new observations using meta-learning. Additionally, the authors introduced a learning algorithm that enables rapid adaptation to new sensor configurations or target objects with a few shots. Notably, their results showed that their approach was able to adapt the learning navigation policy with only three shots for unseen situations using different target colors. In [10], an Actor-Critic network which accepts as input information regarding the current state of the agent and the goal was implemented. The researchers concatenated two convolutional feature vectors using the Hadamard product. The researchers demonstrated using a car navigating in a city as their agent. More so, that combination of state information with goal information allows the agent to learn near-optimal navigation policies. The state representation of the agent at a time step t is defined by a 256 by 651 gray scale image. The autonomous car agent in [10] can perform three actions in its environment, namely, turn left, move forward, and turn right. The authors focused on multigoal learning, and limit their training and testing to the CARLA simulation environment. To navigate in an environment without a map, Reference [26] make use of an LSTM-based A2C network which accepts as input 30 laser scan readings combined with the distance and angle to the agent's goal. The authors demonstrated that the use of an LSTM network enables state-of-the-art navigation and generalization in the partially observable navigation setting. The authors of [26] make use of 2D simulators to train their model in several different environments before placing their learned network parameters onto a real robot. To solve the problem of goal-driven mapless navigation in both aerial and water environments, Reference [27] proposed two Actor-Critic-based network architectures. The networks proposed accept as input 20 range readings, linear velocity of the agent, agent's altitude, yaw, the agent's relative position with regards to the target/goal and the agent's relative angle with regards to the goal. The researchers make use of ROS and Gazebo to train their model in two environments. The agent is trained for 1000 episodes and 2500 episodes, respectively. In Reference [28] the authors make use of A Deep Deterministic Policy Gradient network that maps depth images to actions. The authors highlight that to achieve goal-oriented navigation, the state information \(s_{t}\) must be combined with information about the goal. Reviewing the latest research done on mapless navigation, it becomes apparent that the distance between an agent and its goal plays a very important role in the outcome of the navigation task, this distance is not always known. Supervised learning approaches to navigation as demonstrated by [19] show that using enough data samples, an agent is able to learn the policy mapping visual input to steering commands. The approach taken is 80 percent accurate but requires large amounts of training data. A Deep Neural Network (DNN) is a universal function approximator that can approximate any function given enough data. In their paper, [20] demonstrate how the task can be solved using tabular Q-Learning, which is able to approximate the policy mapping states to actions using a lookup table approach. A state is encountered and the action with the highest \(Q\) value is selected and performed. When using tabular Q-Learning, policies that are able to be learnt are not as expressive as using a DQN and it imposes the limitation that the state representation \(s_{t}\) needs to be in a form that can fit into a table.