  Smart and agile drones are fast becoming ubiquitous at the edge of the cloud.
The usage of these drones are constrained by their limited power and compute
capability. In this paper, we present a Transfer Learning (TL) based approach
to reduce on-board computation required to train a deep neural network for
autonomous navigation via Deep Reinforcement Learning for a target algorithmic
performance. A library of 3D realistic meta-environments is manually designed
using Unreal Gaming Engine and the network is trained end-to-end. These trained
meta-weights are then used as initializers to the network in a test environment
and fine-tuned for the last few fully connected layers. Variation in drone
dynamics and environmental characteristics is carried out to show robustness of
the approach. Using NVIDIA GPU profiler it was shown that the energy
consumption and training latency is reduced by 3.7x and 1.8x respectively
without significant degradation in the performance in terms of average distance
traveled before crash i.e. Mean Safe Flight (MSF). The approach is also tested
on a real environment using DJI Tello drone and similar results were reported.
