This repository is associated to the following publication (under review):
Q. Vacher, N. Beuve, P. Allaire, T. Marty, M. Dardaillon and K. Desnos. Low-complexity Genetic Reinforcement Learning for Robot Arm Trajectory Planning
The repository contains:
- Code and scripts to reproduce the experiments presented in the paper.
- Experimental data and logs produced by the authors and presented in the paper.
├─ sips22-artifacts # root folder
│ │
│ ├─ gegelati # git submodule pointing to gegelati develop commit: 9b4092f
│ │ │...
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│ ├─ armlearn-wrapper # git submodule pointing to the ArmLearn Environment
│ │ │... # wrapper for gegelati.
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│ ├─ Notebook.ipynb # Jupyter Notebook with Julia Kernel for plotting interesting
│ │ # results from experiments.
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│ ├─ data # Experimental data.
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│ │ │
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│ │ ├─ GegelatiExperimentalStudy # Folder containing the performances of all the
│ │ │ │ # configurations tested for 2 hours
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│ │ │ ├─ config_0_0 # One configuration and seed.
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│ │ │ │ ├─ outLogs # Folder of logs.
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│ │ │ │ │ ├─ codeGen # Folder with the code generation
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│ │ │ │ │ ├─ logsGegelati.ods # Logs from the training.
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│ │ │ │ │ ├─ bestPolicyStats.md # Statistics of champion TPGs throughout the generations.
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│ │ │ │ │ ├─ out_best.dot # Champion TPG after all generations.
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│ │ │ │ │ ├─ out_best_cleaned.dot # Champion cleaned from any useless teams and programs.
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│ │ │ │ │ ├─ out_best_stats.md # Statistics about the champion.
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│ │ │ │ │ ├─ out_best_stats_cleaned.md # Statistics about the cleaned chamion.
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│ │ │ │ │ ├─ outputGegelati.csv # Logs from the testing.
│ │ │ │ |
│ │ │ │ ├─ params # Folder with the parameters.
│ │ │ ├─ ...
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│ │ ├─ GegelatiFinalTests # Folder containing the performances of the last
│ │ │ # configuration tested for 12 hours
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│ │ ├─ SAC # Folder containing the performances of the SAC
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│ ├─ results # Folder with the different figures obained