Autonomous Flight Arcade Challenge: Single- and Multi-Agent Learning Environments for Aerial Vehicles

The Autonomous Flight Arcade (AFA) is a novel suite of singleand multi-agent learning environments for control of aerial vehicles. These environments incorporate realistic physics using the Unity game engine with diverse objectives and levels of decisionmaking sophistication. In addition to the environments themselves, we introduce an interface for interacting with them, including the ability to vary key parameters, thereby both changing the difficulty and the core challenges. We also introduce a pipeline for collecting human gameplay within the environments. We demonstrate the performance of artificial agents in these environments trained using deep reinforcement learning, and also motivate these environments as a benchmark for designing non-learned classical control policies and agents trained using imitation learning from human demonstrations. Finally, we motivate the use of AFA environments as a testbed for training artificial agents capable of cooperative human-AI decision making, including parallel autonomy.