MABWiser is a research library written in Python for rapid prototyping of multi-armed bandit algorithms. It supports context-free, parametric and non-parametric contextual bandit models and provides built-in parallelization for both training and testing components. The library also provides a simulation utility for comparing different policies and performing hyper-parameter tuning. MABWiser follows a scikit-learn style public interface, adheres to PEP-8 standards, and is tested heavily. Full documentation is available at fmr-llc.github.io/mabwiser.

MABWiser is developed by the Artificial Intelligence Center of Excellence at Fidelity Investments.

# An example that shows how to use the UCB1 learning policy
# to make decisions between two arms based on their expected rewards.

# Import MABWiser Library
from mabwiser.mab import MAB, LearningPolicy, NeighborhoodPolicy

# Data
arms = ['Arm1', 'Arm2']
decisions = ['Arm1', 'Arm1', 'Arm2', 'Arm1']
rewards = [20, 17, 25, 9]

# Model 
mab = MAB(arms, LearningPolicy.UCB1(alpha=1.25))

# Train
mab.fit(decisions, rewards)

# Test
mab.predict()

Available Learning Policies:

• Epsilon Greedy
• LinTS
• LinUCB
• Softmax
• Thompson Sampling (TS)
• Upper Confidence Bound (UCB1)

Available Neighborhood Policies:

• Clusters
• K-Nearest
• Radius

MABWiser can be installed from the wheel file or building from source by following the instructions in our documentation.

Please submit bug reports and feature requests as Issues.

For additional questions and feedback, please contact us at [email protected].

If you use MABWiser in a publication, please cite it as:

E. Strong, B. Kleynhans, and S. Kadioglu, "MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python," in 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019) (pp.885-890). IEEE, 2019.

    @inproceedings{mabwiser2019,
      author    = {Strong, Emily and Kleynhans, Bernard and Kadioglu, Serdar},
      title     = {MABWiser: A Parallelizable Contextual Multi-Armed Bandit Library for Python},
      booktitle = {2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI 2019)},
      year      = {2019},
      pages     = {885-890},
      organization = {IEEE},
      url       = {https://github.com/fmr-llc/mabwiser}
    }

MABWiser is licensed under the Apache License 2.0.