  This paper presents a Deep Reinforcement Learning based navigation approach
in which we define the occupancy observations as heuristic evaluations of
motion primitives, rather than using raw sensor data. Our method enables fast
mapping of the occupancy data, generated by multi-sensor fusion, into
trajectory values in 3D workspace. The computationally efficient trajectory
evaluation allows dense sampling of the action space. We utilize our occupancy
observations in different data structures to analyze their effects on both
training process and navigation performance. We train and test our methodology
on two different robots within challenging physics-based simulation
environments including static and dynamic obstacles. We benchmark our occupancy
representations with other conventional data structures from state-of-the-art
methods. The trained navigation policies are also validated successfully with
physical robots in dynamic environments. The results show that our method not
only decreases the required training time but also improves the navigation
performance as compared to other occupancy representations. The open-source
implementation of our work and all related info are available at
\url{https://github.com/RIVeR-Lab/tentabot}.
