Companion code for the paper:
Thomas M. McDonald, Lucas Maystre, Mounia Lalmas, Daniel Russo, Kamil Ciosek. Impatient Bandits: Optimizing Recommendations for the Long-Term Without Delay. Proceedings of KDD 2023.
This repository contains
- a reference implementation of the algorithms presented in the paper, and
- Jupyter notebooks providing experiments on synthetic data similar to the ones presentd in the paper.
The paper addresses the problem of optimizing a sequence of decisions for long-term rewards. Assuming that intermediate outcomes correlated with the final reward are revealed progressively over time, we provide a method that is able to take advantage of these intermediate observations effectively.
To get started, follow these steps:
- Clone the repo locally with:
git clone https://github.com/spotify-research/impatient-bandits.git - Move to the repository:
cd impatient-bandits - Install the dependencies:
pip install -r requirements.txt - Install the package:
pip install -e lib/ - Move to the notebook folder:
cd notebooks - Start a notebook server:
jupyter notebok
Our codebase was tested with Python 3.11.3. The following libraries are required
(and installed automatically via the first pip command above):
- notebook (tested with version 6.5.4)
- matplotlib (tested with version 3.7.1)
- numpy (tested with version 1.25.0)
- scipy (tested with version 1.10.1)
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