  With the rapidly growing expansion in the use of UAVs, the ability to
autonomously navigate in varying environments and weather conditions remains a
highly desirable but as-of-yet unsolved challenge. In this work, we use Deep
Reinforcement Learning to continuously improve the learning and understanding
of a UAV agent while exploring a partially observable environment, which
simulates the challenges faced in a real-life scenario. Our innovative approach
uses a double state-input strategy that combines the acquired knowledge from
the raw image and a map containing positional information. This positional data
aids the network understanding of where the UAV has been and how far it is from
the target position, while the feature map from the current scene highlights
cluttered areas that are to be avoided. Our approach is extensively tested
using variants of Deep Q-Network adapted to cope with double state input data.
Further, we demonstrate that by altering the reward and the Q-value function,
the agent is capable of consistently outperforming the adapted Deep Q-Network,
Double Deep Q- Network and Deep Recurrent Q-Network. Our results demonstrate
that our proposed Extended Double Deep Q-Network (EDDQN) approach is capable of
navigating through multiple unseen environments and under severe weather
conditions.
