The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/b68d7aa9-235b-4f8b-b8c8-a977eaceba50

Groot, Jan (2023): Code accompanying the paper on aircraft path planning in continuous environments with deep reinforcement learning. Version 1. 4TU.ResearchData. software. https://doi.org/10.4121/b68d7aa9-235b-4f8b-b8c8-a977eaceba50.v1

Software

• Aerospace Transport
• Artificial Intelligence and Image Processing
• Aerospace Engineering
• Engineering
• Transport
• Information and Communication Services
• Information and Computing Sciences

BlueSky, Deep Reinforcement Learning, Path Planning, Soft Actor Critic

GPL-3.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Jan Groot

This code is used for generating the results shown in the paper on aircraft path planning in continuous environments with deep reinforcement learning.

When the paper is published it will be referenced here.

The code is structured in 3 folders, all using the same population data, obtained from Eurostats. The discrete_environment folder contains all of the code related to the discretization, Dijkstra solutions and postprocessing of the Dijkstra output. The continuous_environment folder contains a fork of the BlueSky Open Air Traffic Simulator repository, with all of the plugins related to training the Deep Reinforcement Learning algorithm, and evaluating of the paths in the continuous environment. Finally the policy_plotter folder contains all of the tools for generating the visuals presented in the paper.

Before running or going through the code make sure to read the ReadMe file.

The software is also available on github: https://github.com/jangroter//PathplanningDRL

• 2023-06-15 first online, published, posted

4TU.ResearchData

Delft University of Technology, Faculty of Aerospace Engineering