Autonomous Flight Arcade: Reinforcement Learning for End-to-End Control of Fixed-Wing Aircraft
Name
Wrafter-dwrafter-meng-eecs-2021-thesis.pdf
Description
Thesis PDF
Size
29.09 MB
Format
Adobe PDF
Checksum (MD5)
8d632a99041dd0da66bf9d3e0c49f4cb
Author(s)
Wrafter, Daniel
Advisor(s)
Rus, Daniela L.
Date Issued
June 2021
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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