2026-09-20T08:58:44Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1392972022-01-15T03:27:43Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Rus, Daniela L.
Wrafter, Daniel
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2022-01-14T15:02:18Z
2022-01-14T15:02:18Z
2021-06
2021-06-17T20:14:50.020Z
https://hdl.handle.net/1721.1/139297
In this paper, we present the Autonomous Flight Arcade (AFA), a suite of robust environments for end-to-end control of fixed-wing aircraft and quadcopter drones. These environments are playable by both humans and artificial agents, making them useful for varied tasks including reinforcement learning, imitation learning, and human experiments. Additionally, we show that interpretable policies can be learned through the Neural Circuit Policy architecture on these environments. Finally, we present baselines of both human and AI performance on the Autonomous Flight Arcade environments.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Autonomous Flight Arcade: Reinforcement Learning for End-to-End Control of Fixed-Wing Aircraft
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>In this paper, we present the Autonomous Flight Arcade (AFA), a suite of robust environments for end-to-end control of fixed-wing aircraft and quadcopter drones. These environments are playable by both humans and artificial agents, making them useful for varied tasks including reinforcement learning, imitation learning, and human experiments. Additionally, we show that interpretable policies can be learned through the Neural Circuit Policy architecture on these environments. Finally, we present baselines of both human and AI performance on the Autonomous Flight Arcade environments.</Abstract>
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