🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on
It's been 6 months since the last release, and I didn't implement any new features. I have been using SMPyBandits a lot in the last few months, when I was writing my PhD thesis. I fixed a lot of small issues, but I didn't have the time to do more.
Almost done for piece-wise stationary bandits. Still some issues to close, see:
LM-DSEE
seems bugged, cf. #151PieceWiseStationaryMAB
for best arm pull selections, cf. #153I have opened this project since more than 6 months, I wrote a companion research paper to present it, its documentation is now handled automatically by ReadTheDocs (see it live here), Travis CI is used to test every commit, etc.
SMPyBandits has been used in more research articles, it received its first other contributor (thanks @guilgautier !), and many other cool things happened. I have kept SMPyBandits up-to-date with my (small and partial) point of view on the state-of-the-art research in classical or multi-player multi-armed bandit algorithms and heuristics.
I met researchers in a workshop in Rotterdam (Netherlands) and in a workshop in Toulouse (France) who knew my library and my work but didn't know me, and told me that they found this work useful. Colleagues in Inria Lille has used SMPyBandits, for teaching or research, and I was happy to help them and learn from them.
Please keep in mind that this is only meant as a research framework: easy to interact with, easy to modify, and easy to do some small or medium-sized simulations and get nice figures for research paper.
It is not meant as an industry package for multi-armed bandits. If you want to use any MAB algorithms for real-world content optimization, you should rather implement them yourself to better suit your needs.
With that being said, I am still excited to share this project on GitHub, now on its own organization instead of my personal profile. If you have any suggestion on how I could improve this project, I would be delighted to here them! Contributions like issues, pull requests, questions etc are welcome.
I finally open-sourced my research framework on multi-armed bandits 🎉 https://github.com/Naereen/AlgoBandits
Please keep in mind that this is only meant as a research framework: easy to interact with, easy to modify, and easy to do some small or medium-sized simulations and get nice figures for research paper.
It is not meant as an industry package for multi-armed bandits. If you want to use any MAB algorithms for real-world content optimization, you should rather implement them yourself to better suit your needs.
With that being said, I am very excited to finally share this on GitHub. If you have any suggestion on how I could improve this project, I would be delighted to here them! Contributions like issues, pull requests, questions etc are welcome.