Open-Source Python package for Single- and Multi-Players multi-armed Bandits algorithms.
A research framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms: UCB, KL-UCB, Thompson and many more for single-players, and MCTopM & RandTopM, MusicalChair, ALOHA, MEGA, rhoRand for multi-players simulations. It runs on Python 2 and 3, and is publically released as an open-source software under the MIT License.
Note
See more on the GitHub page for this project: https://github.com/SMPyBandits/SMPyBandits/. The project is also hosted on Inria GForge, and the documentation can be seen online at https://smpybandits.github.io/ or http://http://banditslilian.gforge.inria.fr/ or https://smpybandits.readthedocs.io/.
This repository contains the code of my numerical environment, written in Python, in order to perform numerical simulations on single-player and multi-players Multi-Armed Bandits (MAB) algorithms.
I (Lilian Besson) have started my PhD in October 2016, and this is a part of my on going research since December 2016.
If you use this package for your own work, please consider citing it with this piece of BibTeX:
@misc{SMPyBandits, title = {{SMPyBandits: an Open-Source Research Framework for Single and Multi-Players Multi-Arms Bandits (MAB) Algorithms in Python}}, author = {Lilian Besson}, year = {2018}, url = {https://github.com/SMPyBandits/SMPyBandits/}, howpublished = {Online at: \url{GitHub.com/SMPyBandits/SMPyBandits}}, note = {Code at https://github.com/SMPyBandits/SMPyBandits/, documentation at https://smpybandits.github.io/} }
I also wrote a small paper to present SMPyBandits, and I will send it to JMLR MLOSS. The paper can be consulted here on my website.
.. toctree:: :maxdepth: 1 :caption: Contents: README.md docs/modules.rst How_to_run_the_code.md PublicationsWithSMPyBandits.md Aggregation.md MultiPlayers.md DoublingTrick.md SparseBandits.md NonStationaryBandits.md API.md About_parallel_computations.md TODO.md plots/README.md notebooks/README.md notebooks/list.rst Policies/C/README.md Profiling.md uml_diagrams/README.md logs/README.md CONTRIBUTING.md CODE_OF_CONDUCT.md
Note
Both this documentation and the code are publicly available, under the open-source MIT License. The code is hosted on GitHub at github.com/SMPyBandits/SMPyBandits.