  The advent of deep learning (DL) gave rise to significant breakthroughs in
Reinforcement Learning (RL) research. Deep Reinforcement Learning (DRL)
algorithms have reached super-human level skills when applied to vision-based
control problems as such in Atari 2600 games where environment states were
extracted from pixel information. Unfortunately, these environments are far
from being applicable to highly dynamic and complex real-world tasks as in
autonomous control of a fighter aircraft since these environments only involve
2D representation of a visual world. Here, we present a semi-realistic flight
simulation environment Harfang3D Dog-Fight Sandbox for fighter aircrafts. It is
aimed to be a flexible toolbox for the investigation of main challenges in
aviation studies using Reinforcement Learning. The program provides easy access
to flight dynamics model, environment states, and aerodynamics of the plane
enabling user to customize any specific task in order to build intelligent
decision making (control) systems via RL. The software also allows deployment
of bot aircrafts and development of multi-agent tasks. This way, multiple
groups of aircrafts can be configured to be competitive or cooperative agents
to perform complicated tasks including Dog Fight. During the experiments, we
carried out training for two different scenarios: navigating to a designated
location and within visual range (WVR) combat, shortly Dog Fight. Using Deep
Reinforcement Learning techniques for both scenarios, we were able to train
competent agents that exhibit human-like behaviours. Based on this results, it
is confirmed that Harfang3D Dog-Fight Sandbox can be utilized as a 3D realistic
RL research platform.
