Automated Aircraft Touchdown
نویسندگان
چکیده
In this paper, we explore the challenging and important problem of automated aircraft landing. We created a simulator to be able to perform online experiments in various conditions including stormy weather and starting from off-trajectory starting points. We experiment with a wide range of techniques to learn the optimal flight policy including reinforcement learning, Monte Carlo based dynamic programming and direct policy search; and through our experiments we are able to analyze the efficiency of these strategies with respect to the problem at hand. We also discuss our use of parallelization to speed up our computations in cases applicable, and the tradeoff between accuracy and run time. You can find the code for each of the learning algorithms and the simulator at : https://github.com/sagarvare/ CS238-Automated-Aircraft-Landing-Reinforcement-Learning.git
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تاریخ انتشار 2016