Synthetic Personalization Environment

This repository contains the source code for the numerical experiments presented in the paper On the Unreasonable Efficiency of State Space Clustering in Personalization Tasks.

Installation

• Install the requirements via pip install -r requirements.txt
• Configure the environment in config.yaml
• Run the experiments via python -m run_experiments

Files Overview

• synthetic_gaussian_mapping.py --- creates the Synthetic Gaussian Mapping that acts as a latent feature extractor for the simulated reward signal
• bandit_environment.py --- creates the Synthetic Hyperpersonalization Environment with the simulated reward signal as an OpenAI Gym environment
• online_rl.py --- trains online RL algorithms on a given environment
• run_experiments.py --- sets up and runs the experiments
• config.yaml --- stores the environment/training/experiment parameters
• requirements.txt --- lists the required packages

License

This project is licensed under the MIT License.