Decentralized assembly of arbitrary formations using identical agents

AE4350 Assignment

by Thomas Wagenaar

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This repository provides all the code for generating the results and the subsequent figures used in my report for the course AE4350 Bio-inspired intelligence and learning for aerospace applications. The size of this repository is fairly large (1.5+ GB), but this is simply because of the datafiles necessary to store the results. To save space, one can clone everything but the /results folder.

⏩ How to run

First of all, the code was developed in Python 3.8.5 with the packages listed in requirements.txt. If you have Python 3.9+, you might have to change some imports of pymoo but everything should work. To start a single optimization run, only three files are necessary:

• base_config.json: contains all the hyperparameters
• evaluation.py: provides the fitness evaluation functions
• train.py: implements CMA-ES for optimization

For a simple run, only these steps are necessary:

1. Set the hyperparameters in base_config.json
2. Run train.py

For the given settings in base_config.json, training should take approximately 90 minutes. After training has completed, a /results folder will have been created with the start timestamp. In the folder one can find three files:

• config.json: a copy of the hyperparameters for later use
• fitnesses.dat: the best fitness for each epoch
• genomes.dat: parameters of the genome with the best fitness for each epoch

The latter two files can be loaded with np.fromfile() (not np.load()).

 

💾 Folder explanation

Although the three files mentioned above are all that is necessary to perform the optimization procedure, some other scripts are present for batch training and result analysis. A small overview of the contents of each folder is given below:

• /analysis: contains the scripts that generated the figures for the report
• /figures: output directory of the above scripts, contains the figures
• /results: contains existing results from the batch training scripts
• /train_scripts: scripts for batch training, i.e. they perform multiple optimization runs with different settings

Note! If you want to run any scripts in this repository, it must be done from the top repository directory. Do not cd into directories to run the scripts.

 

📫 Contact

t.wagenaar-1@student.tudelft.nl