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Code to reproduce "Deep reinforcement learning for six degree-of-freedom planetary powered descent and landing"

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l6jj/AAS-18-290-6DOF

 
 

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AAS-18-290-6DOF: Preprint Results

AAS-18-290-6DOF_manuscript: Final Results

Please cite the following paper if you publish work using this code:

@article{gaudet2020deep, title={Deep Reinforcement Learning for Six Degree-of-Freedom Planetary Landing}, author={Gaudet, Brian and Linares, Richard and Furfaro, Roberto}, journal={Advances in Space Research}, year={2020}, publisher={Elsevier} }

Note you may need to run several optimizations to achieve results comparable to that in the paper

virtual environment should include:

Tensorflow

Python 3.5

Matplotlib 2.1.1 (newer versions mess up the plotting in notebook)

Scipy

scikit-learn

jupyter

1.) Download this repository

2.) cd to Run/Run_4km_terminal

3.) open jupyter from the virtual environment

4.) run the notebook

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Code to reproduce "Deep reinforcement learning for six degree-of-freedom planetary powered descent and landing"

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  • Jupyter Notebook 97.6%
  • Python 2.4%