## 5 Conclusions and Future Work  
Mapless navigation refers to navigation of an agent in an environment without a model of the environment. The distance to the goal in a mapless navigation task is used to inform the agent's navigation. The agent can use this distance measure to select actions that bring it closer towards its goal. In simulated environments, obtaining this distance is trivial. In real-world settings this distance is found using localization techniques such asLIDAR and GPS. For agents in the real world not equipped with localization sensors, the mapless navigation task cannot rely on this distance measure to select actions. In this paper, we have demonstrated how a pre-trained SCNN can be used to estimate the distance between an agent and its goal, enabling agents equipped with onboard cameras to navigate in an unknown environment without the need for localization sensors. This technique can be applied to environments where the location of a goal may be anonymous, and the only information regarding the goal may be a description of the goal state. Examples of such environments are:  
* An agent tasked with locating a bomb may not know where the bomb is but may have some examples of what a bomb looks like;
* An agent tasked with helping an elderly person navigate their environment may not know where all the obstacles/items are located around it but it has a description given to it of what it needs to find;
* An agent tasked with guarding a premises from intruders may not know where the intruder is located but given examples of previous intrusions, it can locate and detail the intruder. As future research perspectives, it is worth noting that our agent was able to make informed decisions that bring it closer to its goal using the SCNN. Therefore, we would like to highlight here some promising areas of future improvement upon our agent design, namely:  
* LSTM layers in the DQN architecture would enable a sort of memory for an agent and would make more of the environment 'visible' to the agent at each \(t\). The inclusion of LSTM layers would increase the complexity of the network and may increase time needed to learn the task. * Using visual input as a state representation can be difficult when the goal is blocked by obstacles in the environment and is not visible to the agent. In future works we would like to incorporate more information into \(s_{t}\) to overcome the problem of partial observable environments. * In this article the two networks used are treated separately. In future work, the networks may be combined and their parameters updated as one. This would enable learning the task 'on-the-fly'. * In this work, we have made use of a pre-trained SCNN which may still require effort to collect a few samples of data for pre-training. We have taken a few-shot learning approach, in future works one-shot and zero-shot approaches can be explored to reduce the need for a dataset for pre-training. * We make use of the Euclidean distance to calculate the distance between the embedding produced by the SCNN. In future we would like to explore the effects of various distance metrics on the navigation task. * Imitation learning is a technique to learn models from a human teacher. In future works on mapless navigation we would like to explore a few-shot variant of imitation learning which is able to learn navigation policies from a single demonstration of navigation in an environment. * The design of our agent was kept simple to demonstrate cost-effective mapless navigation in indoor environments. This may be enhanced to include outdoor environments with varying terrain and conditions by incorporating larger wheels with a track or tyre and stronger motors. * The current study focused primarily on the DQN algorithm as a result of its relatively simple and intuitive nature. Since its inception, the DQN has been improved upon and as such we would like to test how our method performs when used with other algorithms from the RL literature.