  We introduce AAM-Gym, a research and development testbed for Advanced Air
Mobility (AAM). AAM has the potential to revolutionize travel by reducing
ground traffic and emissions by leveraging new types of aircraft such as
electric vertical take-off and landing (eVTOL) aircraft and new advanced
artificial intelligence (AI) algorithms. Validation of AI algorithms require
representative AAM scenarios, as well as a fast time simulation testbed to
evaluate their performance. Until now, there has been no such testbed available
for AAM to enable a common research platform for individuals in government,
industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address
this gap by providing an ecosystem to develop, train, and validate new and
established AI algorithms across a wide variety of AAM use-cases. In this
paper, we use AAM-Gym to study the performance of two reinforcement learning
algorithms on an AAM use-case, separation assurance in AAM corridors. The
performance of the two algorithms is demonstrated based on a series of metrics
provided by AAM-Gym, showing the testbed's utility to AAM research.
