interpole

Explaining by Imitating: Understanding Decisions by Interpretable Policy Learning

Code author: Alihan Hüyük (ah2075@cam.ac.uk)

This repository contains the necessary code to replicate the main experimental results in the ICLR 2021 paper 'Explaining by Imitating: Understanding Decision by Interpretable Policy Learning.' Our proposed method, Interpole, is implemented in files adni/main-interpole.py and diag-bias/main-interpole.py for the decision environments considered in the paper, namely ADNI, DIAG, and BIAS.

Usage

First, install pomdp-solve v5.4 inside the empty directory pomdp/ by following the instructions on pomdp.org. Make sure the executable pomdp-solve is located at pomdp/src/pomdp-solve. Install the required python packages as well by running:

    python3 -m pip install -r requirements.txt

Then, the experiments in the paper can be replicated by running:

    ./adni/run.sh            # generates the results for ADNI given in Table 2
    ./diag-bias/run.sh       # generates the results for DIAG given in Table 3
    ./diag-bias/run-bias.sh  # generates the results for BIAS given in Table 4

Note: in order to run the adni experiment, you need to get access to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.

Citing

If you use this software please cite as follows:

@inproceedings{huyuk2021explaining,
  author={Alihan Huyuk and Daniel Jarrett and Cem Tekin and Mihaela van der Schaar},
  title={Explaining by imitating: understanding decisions by interpretable policy learning},
  booktitle={Proceedings of the 9th International Conference on Learning Representations (ICLR)},
  year={2021}
}