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This is the code for the design, evaluation and implementation of contextual bandits to learn optimal physical exercises regimes that decrease pain in endometriosis patients.

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HIAlab/Reinforcement-learning-agents-in-N-of-1-trials

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This is the code for the design, evaluation and implementation of contextual bandits to learn optimal physical exercises regimes that decrease pain in endometriosis patients.

For more information, see our arxiv submission.

HTML Output

If you do not want to setup the python environment, evaluation.html still allows an interactive view of the intervention allocations in the browser.

Setup

To run the jupyter notebook, you can install the necessary dependencies with:

python3 -m pip install -e .[dev]

Now, to start a jupyter lab, run

jupyter lab

We provide the used dataset from the paper under data/2023-09-20-series.json.

Evaluation

The evaluation notebook can be found under notebooks/evaluation.ipynb.

Data Generation

The data was generated using a custom simulation library. You can view the setup of the bayesian and data generating models in pymc under notebooks/data-generation.ipynb.

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This is the code for the design, evaluation and implementation of contextual bandits to learn optimal physical exercises regimes that decrease pain in endometriosis patients.

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