Python library for Multi-Armed Bandits

Implements the following algorithms:

• Epsilon-Greedy
• UCB1
• Softmax
• Thompson Sampling (Bayesian)
  • Bernoulli, Binomial <=> Beta Distributions

• Bayesian Belief
• Value Estimation Methods
• Preference Estimation

• Multi-Armed Bandit
• Conjugate Prior

• When to Run Bandit Tests Instead of A/B/n Tests
• Bandit theory, part I
• Bandit theory, part II
• Bandits for Recommendation Systems
• Recommendations with Thompson Sampling
• Personalization with Contextual Bandits
• Bayesian Bandits - optimizing click throughs with statistics
• Mulit-Armed Bandits
• Bayesian Bandits
• Python Multi-armed Bandits (and Beer!)

• Boston Bayesians Meetup 2016 - Bayesian Bandits From Scratch
• ODSC East 2016 - Bayesian Bandits
• NYC ML Meetup 2010 - Learning for Contextual Bandits

• Reinforcement Learning: An Introduction
• Multi-armed Bandit Allocation Indices
• Bandit Algorithms for Website Optimization
• Multi-Armed Bandit Problems (in Foundations and Applications of Sensor Management)

• A Survey on Contextual Multi-armed Bandits

• Bayesian Bandit Explorer (simulation)
• Yelp MOE
• Bandit Algorithms for Website Optimization (code)