The major components of any successful autonomous flight system are task completion and collision avoidance. Most deep learning algorithms are successful while executing these aspects under the environment and conditions in which they have been trained. However, they fail when subjected to novel environments. In this paper we present autonomous UAV flight using Deep Reinforcement Learning augmented with Self-Attention Models  that can effectively reason when subjected to varying inputs. In addition to their reasoning ability, they also are interpretable which enables it to be used under real-world conditions. We have tested our algorithm under different weather  and environments and found it to be robust compared to conventional Deep Reinforcement Learning algorithms.
The significant components of any successful autonomous flight system are task completion and collision avoidance. Most deep learning algorithms successfully execute these aspects under the environment and conditions they are trained. However, they fail when subjected to novel environments. This paper presents an autonomous multi-rotor flight algorithm, using Deep Reinforcement Learning augmented with Self-Attention Models , that can effectively reason when subjected to varying inputs. In addition to their reasoning ability, they are also interpretable, enabling it to be used under real-world conditions. We have tested our algorithm under different weather conditions and environments and found it robust compared to conventional Deep Reinforcement Learning algorithms.
R
major
significant
R
are successful while executing
successfully execute
R
in which they have been
they are
R
In this paper we present autonomous UAV flight
This paper presents an autonomous multi-rotor flight algorithm,
A
,
R
also are interpretable which enables
are also interpretable, enabling
A
conditions
D
to be