SebastianoF/multi-armed-bandits-testbed

Multi amred bandits testbed

Multi armed bandit

An implementation of a range of epsilon-greedy algorithms to solve the multi-armed bandits problem.

Install

• Clone the repo locally:

git clone git@github.com:SebastianoF/multi-armed-bandits-testbed.git
cd multi-armed-bandits-testbed

• Create and activate a virtual environment:

virtualenv -p python3.9 venv
source venv/bin/activate

The library is tested with python 3.9, 3.10, 3.11. Could work with previous version downgrading manually numpy to a compatible version.

• Install in development mode:

pip install -e .

• To install the development third party requirement packages along with the basic ones:

pip install -e .["dev"]

Where to start

Check out the examples in the folder /examples/start_here.py.

Development

• Dependent libraries are managed with pip-compile-multi
• Continuous integration is integrated with CircleCI
• Code formatting happens via makefile command make reformat, after installing the dev packages.

Resources

• R. Sutton, A. Barto, "Reinforcement Learning, an introduction", Chapter 1.

• Further introduction can be found in the folder /docs/

Licence

Repository open-sourced under MIT licence.