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This repository contains code used to conduct experiments reported in the paper "Personalized Reward Learning with Interaction-Grounded Learning" published at ICLR 2023.

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Personalized Reward Learning with Interaction-Grounded Learning

This respository contains code for the following paper:

J. Maghakian, P. Mineiro, K. Panaganti, M. Rucker, A. Saran, C. Tan. Personalized Reward Learning with Interaction-Grounded Learning. International Conference on Learning Representations (ICLR), May 2023.

Motivating 3-state IGL

cd motivating_3state_IGL
ipython -c "%run IGL_3_states_motivation.ipynb"
ipython -c "%run IGL_3_states.ipynb"
ipython -c "%run IGL_compare_pdislikes.ipynb"

Covertype Simulation

cd covertype
ipython -c "%run covertype.ipynb"

Facebook News Recommendation Simulation

cd fb_news
ipython -c "%run fb_news_experiment.ipynb"

Bibliography

If you find our work to be useful in your research, please cite:

@inproceedings{maghakian2023personalized,
  title={Personalized Reward Learning with Interaction-Grounded Learning},
  author={Maghakian, Jessica and Mineiro, Paul and Panaganti, Kishan
  and Rucker, Mark and Saran, Akanksha and Tan, Cheng},
  booktitle={International Conference on Learning Representations (ICLR)},
  year={2023}
}

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This repository contains code used to conduct experiments reported in the paper "Personalized Reward Learning with Interaction-Grounded Learning" published at ICLR 2023.

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