Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization

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This repogistory is the implementation of Gaussian Max-Value Entropy Search for Multi-Agent Bayesian Optimization accepted to [IROS 2023].

The code is modified from Distributed Bayesian Optimization for Multi-Agent Systems created by Filip Klaesson. For the baselines, the original documentation has a more clear explaination about the code. The updates are very similar to the original code. If you have specific questions regarding my updates of the code, feel free to open an issue or email me.

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dbo is a compact python package for bayesian optimization. It utilizes scikit-learn and gpytorch to model the surrogate function and offers multiple strategies to select queries. In addition to the vanilla bayesian optimization algorithm, dbo offers:

• Distributed and parallel optimization
• The proposed Gaussian max-value entropy search algorithm in our paper.
• Stochastic policy evaluations
• Expected acquisition policy over pending queries
• Internal acquisition function regularization
• Regret analysis outputs

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Installation

This setup guide is for Debian-like environments. Note that if you would like to use GPU acceleartion for pytorch, please fist refer to the official documentation to install pytorch and then install dependencies in the requirements.txt.

git clone https://github.com/mahaitongdae/dbo.git && cd dbo
pip install -r requirements.txt
python setup.py install     # use 'python setup.py develop' for development

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Running experiments

cd examples
python main_2d_json.py

OR for batch experiments

cd examples
chmod +x experiments.sh
./experiments.sh

Output

dbo will create a results folder in the same directory as __main__. The output (generated data/plots/gifs) will be stored in the results folder keyed with date and time. For example, by running dbo with __main__ in the dbo project folder, the directory will look like this:

dbo
└───src  
└───examples
└───results
    └───$TEST_FUNCTION_NAME
        └───$ALGO_NAMEYYYY-MM-DD_HH:MM:SS
            └───data
            └───fig
                └───png
                └───pdf
                └───gif

Running optimize() will generate the following output:

• regret

  • File with mean regret over the n_runs together with the 95% confidence bound error (.csv)

    • Note: The 95% confidence bound is the symmetric bound on the linear scale.

  • Plots of mean regret together with 95% confidence bounds (.png/.pdf)

• bo*

  • Plots of every iteration in the optimization algorithm (.png/.pdf)

  • Gif of the progress in the optimization algorithm (.gif)

Plots and gifs (except regret plot) are disabled when n_runs > 1.

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Contributing

Free free to contribute via issues or pull requests.

Credits

Thank Filip Klaesson for the original implementations.