Autonomous Flight Arcade: Reinforcement Learning for End-to-End Control of Fixed-Wing Aircraft

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...

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Detalhes bibliográficos
Autor principal: Wrafter, Daniel
Outros Autores: Rus, Daniela L.
Formato: Tese
Publicado em: Massachusetts Institute of Technology 2022
Acesso em linha:https://hdl.handle.net/1721.1/139297
Descrição
Resumo: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.