Bandit algorithms
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Updated
Oct 12, 2017 - Python
Bandit algorithms
Yahoo! news article recommendation system by linUCB
A open source multi arm bandit framework for optimize your website quickly. You’ll quickly use the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through this framework written in Java, which you can easily adapt for deployment on your own website.
🐍 🔬 Fast Python implementation of various Kullback-Leibler divergences for 1D and 2D parametric distributions. Also provides optimized code for kl-UCB indexes
💫 Fast Julia implementation of various Kullback-Leibler divergences for 1D parametric distributions. 🏋 Also provides optimized code for kl-UCB indexes
Implementation of famous Bandits algortihm: Explore then commit, UCB & Thompson sampling in python.
Python implementation of UCB, EXP3 and Epsilon greedy algorithms
A collection of implementations of the bandit problem.
Contextual Bandit algorithms for Warfarin Treatment
More about the exploration-exploitation tradeoff with harder bandits
Personalized and Interactive Music Recommendation with Bandit approach
Ads Click-through rate using thompson sampling
Creation of filters using electric passive elements
a collection of google colab notebooks with educational stuff about bandits and their variations
AI Reinforcement Learning in Python
DPE code - Code used in "Optimal Algorithms for Multiplayer Multi-Armed Bandits" (AISTATS 2020)
Bandit algorithms in OCaml
Source code for blog post on Thompson Sampling
Repo of the Data Science Project course (5AIASX05)
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