  The proliferation of unmanned aircraft systems (UAS) has caused airspace
regulation authorities to examine the interoperability of these aircraft with
collision avoidance systems initially designed for large transport category
aircraft. Limitations in the currently mandated TCAS led the Federal Aviation
Administration to commission the development of a new solution, the Airborne
Collision Avoidance System X (ACAS X), designed to enable a collision avoidance
capability for multiple aircraft platforms, including UAS. While prior research
explored using deep reinforcement learning algorithms (DRL) for collision
avoidance, DRL did not perform as well as existing solutions. This work
explores the benefits of using a DRL collision avoidance system whose
parameters are tuned using a surrogate optimizer. We show the use of a
surrogate optimizer leads to DRL approach that can increase safety and
operational viability and support future capability development for UAS
collision avoidance.
