- Create a virtualenv or conda environment
- Run
pip install -r requirements.txt
- Prepare the dataset by running
python clean_dataset.py
to generate the version of the data that our models will run.
To run an agent use python main.py --agent=AGENT_NAME
to generate a set of 20 experiments. Examples of agents are fixed, linear, ucb, linucb, supervised-lin, supervised-ridge, thompson-0, ensemble-0
Run python plot.py --agent=AGENT_NAME1,AGENT_NAME2
to visualize that agent's performance against the
other agents provided. This automatically generates the named plots in the plots/ directory.