  In recent years, Deep Reinforcement Learning emerged as a promising approach
for autonomous navigation of ground vehicles and has been utilized in various
areas of navigation such as cruise control, lane changing, or obstacle
avoidance. However, most research works either focus on providing an end-to-end
solution training the whole system using Deep Reinforcement Learning or focus
on one specific aspect such as local motion planning. This however, comes along
with a number of problems such as catastrophic forgetfulness, inefficient
navigation behavior, and non-optimal synchronization between different entities
of the navigation stack. In this paper, we propose a holistic Deep
Reinforcement Learning training approach in which the training procedure is
involving all entities of the navigation stack. This should enhance the
synchronization between- and understanding of all entities of the navigation
stack and as a result, improve navigational performance. We trained several
agents with a number of different observation spaces to study the impact of
different input on the navigation behavior of the agent. In profound
evaluations against multiple learning-based and classic model-based navigation
approaches, our proposed agent could outperform the baselines in terms of
efficiency and safety attaining shorter path lengths, less roundabout paths,
and less collisions.

Robotics;Machine Learning