saeedghoorchian / ncc-bandits

Experiments for paper "Online Learning with Costly Features in Non-stationary Environments"

Online Learning with Costly Features in Non-stationary Environments

This repository contains source code for the paper "Online Learning with Costly Features in Non-stationary Environments". The source code includes:

• Implementation of the NCC-UCRL2 algorithm presented in the paper.
• Benchmark bandit algorithms: Epsilon-greedy, UCB1, LinUCB, PS-LinUCB, Sim-OOS.
• Experiments on the UCI Nursery dataset (https://archive.ics.uci.edu/ml/datasets/nursery).

You can use this repository to reproduce our results or try other algorithms, datasets or settings.

Getting started

Clone this repository:

git clone https://github.com/SaeedGhoorchian/costly_nonstationary_bandits
cd costly_nonstationary_bandits 

Setup the environment:

The environment.yml file describes the list of library dependencies. For a fast and easy way to setup the environment we encourage you to use Conda. Use the following commands to install the dependencies in a new conda environment and activate it:

conda env create -f environment.yml
conda activate costly_nonstationary_bandits

Reproduce the experiments:

Run the notebook reproducing/reproducing_nursery.ipynb to reproduce the experiments and figures from the paper.

This notebook should serve as an entry point for understanding the data and the code used in the paper. It consists of three parts:

• Preparing the dataset. This includes preprocessing it to fit MAB setting, introducing costs and non-stationarity.
• Evaluating all the bandit algorithms in a simulated environment using the prepared data.
• Plotting the figures using the evaluation data.