[Submitted on 26 Apr 2020]

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Abstract: Over the past decades, progress in deployable autonomous flight systems has
slowly stagnated. This is reflected in today’s production air-crafts, where
pilots only enable simple physics-based systems such as autopilot for takeoff,
landing, navigation, and terrain/traffic avoidance. Evidently, autonomy has not
gained the trust of the community where higher problem complexity and cognitive
workload are required. To address trust, we must revisit the process for
developing autonomous capabilities: modeling and simulation. Given the
prohibitive costs for live tests, we need to prototype and evaluate autonomous
aerial agents in a high fidelity flight simulator with autonomous learning
capabilities applicable to flight systems: such a open-source development
platform is not available. As a result, we have developed GymFG: GymFG couples
and extends a high fidelity, open-source flight simulator and a robust agent
learning framework to facilitate learning of more complex tasks. Furthermore,
we have demonstrated the use of GymFG to train an autonomous aerial agent using
Imitation Learning. With GymFG, we can now deploy innovative ideas to address
complex problems and build the trust necessary to move prototypes to the
real-world.