Title: NAVREN-RL: Learning to fly in real environment via end-to-end deep reinforcement learning using monocular images,
Abstract: We present NAVREN-RL, an approach to NAVigate an unmanned aerial vehicle in
an indoor Real ENvironment via end-to-end reinforcement learning RL. A suitable
reward function is designed keeping in mind the cost and weight constraints for
micro drone with minimum number of sensing modalities. Collection of small
number of expert data and knowledge based data aggregation is integrated into
the RL process to aid convergence. Experimentation is carried out on a Parrot
AR drone in different indoor arenas and the results are compared with other
baseline technologies. We demonstrate how the drone successfully avoids
obstacles and navigates across different arenas.