0.1.0-alpha

PyMAB (Python Multi-Armed Bandit) is an experimental framework for comparing multiple Multi-Armed Bandit algorithms and configurations. This alpha release provides researchers and practitioners with a flexible foundation for bandit-based experimentation and analysis. New algorithms and improvements coming soon.

Algorithms:

• Greedy Policy
• Epsilon-Greedy Policy
• UCB
• Bayesian UCB with Gaussian/Bernoulli distributions
• Thompson Sampling (Gaussian/Bernoulli variants)
• Contextual Bandits

Environment Types:

• Stationary
• Non-Stationary:
  • Gradual distribution changes
  • Abrupt distribution changes
  • Random arm swapping

Known limitations:

• Some policies (Softmax, Gradient) pending implementation
• API may undergo changes based on feedback
• Lack of parallelisation and other optimisations