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LTO-CMA

Code for the paper "Learning Step-Size Adaptation in CMA-ES"

License

Our work is available under Apache-2.0. In order to learn step-size adaptation in CMA-ES we use guided policy search (GPS). We built upon the GPS version as given by Li and Malik. The original GPS code of Li and Malik can be found at https://www.math.ias.edu/~ke.li/downloads/lto_code.tar.gz

In a nutshell, we modified the GPS code to be able to continuously sample from the starting teacher. To this end we introduce a sampling rate that determines how often we use new samples generated from the starting policy.

The original code falls under GPLv3. In source/gps we list files that we modifed (thereby fall under GPLv3) and those that are of our creation (i.e. under Apache-2.0)

Experiment Setup

Training

  • Create experiment folder
  • Create file with hyperparameters of the experiment hyperparams.py in the experiment folder
  • Start learning step-size adaptation by executing the command:
python gps_train.py EXPERIMENT_FOLDER_NAME
  • The output of training is the pickled version of the learned policy, saved in the path EXPERIMENT_FOLDER_NAME/data_files.

Testing

  • Add the path to the learned policy in the hyperparameter file hyperparams.py
  • Start testing the performance of the learned policy on the test set by executing the command:
python gps_test.py EXPERIMENT_FOLDER_NAME
  • The output of testing are the files log_data_test_X.json for the function name of each condition X of the test set, saved in the experiment folder.
  • The output file test_data_X.json contains:
    • The average objective values from 25 samples of running the learned policy on the test condition X,
    • The end objective values of the 25 samples,
    • The average step-size for each step of the optimization trajectory from 25 samples, and
    • The standard deviation of the objective value and the step-size for each step of the optimization trajectory.
  • To plot the results, run the plot_performance.py script in the scripts folder.

Reference

@inproceedings{shala-ppsn20,
  author    = {G.Shala and A. Biedenkapp and N.Awad and S. Adriaensen and M.Lindauer and F. Hutter},
  title     = {Learning Step-size Adaptation in CMA-ES},
  booktitle = {Proceedings of the Sixteenth International Conference on Parallel Problem Solving from Nature ({PPSN}'20)},
  year = {2020},
  month     = sep,
}

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Code for the paper "Learning Step-Size Adaptation in CMA-ES"

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