  In recent years, mobile robot navigation approaches have become increasingly
important due to various application areas ranging from healthcare to warehouse
logistics. In particular, Deep Reinforcement Learning approaches have gained
popularity for robot navigation but are not easily accessible to non-experts
and complex to develop. In recent years, efforts have been made to make these
sophisticated approaches accessible to a wider audience. In this paper, we
present Arena-Web, a web-based development and evaluation suite for developing,
training, and testing DRL-based navigation planners for various robotic
platforms and scenarios. The interface is designed to be intuitive and engaging
to appeal to non-experts and make the technology accessible to a wider
audience. With Arena-Web and its interface, training and developing Deep
Reinforcement Learning agents is simplified and made easy without a single line
of code. The web-app is free to use and openly available under the link stated
in the supplementary materials.

Robotics;Artificial Intelligence;Human-Computer Interaction;Machine Learning