Bandit algorithms
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Updated
Oct 12, 2017 - Python
Bandit algorithms
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.
Implementations of basic concepts dealt under the Reinforcement Learning umbrella. This project is collection of assignments in CS747: Foundations of Intelligent and Learning Agents (Autumn 2017) at IIT Bombay
Implementation scripts of Machine Learning algorithms on Scikit-learn and Keras for complete novice..
python implementation of e-Greedy, UCB, LinUCB, LinThompson, and offline evaluator
End to end reinforcement learning based recommendation system.
Data Intelligence Application project
Implementation of the FeedBack Adaptive Learning (FeedBAL) algorithm for the episodic multi-armed bandit (eMAB) setting.
University of Utah—MKTG 66420 | Taken: Fall 2020
Review project on Information Directed Sampling - MVA MSc
An introduction to multi arm bandits
Client that handles the administration of StreamingBandit online, or straight from your desktop. Setup and run streaming (contextual) bandit experiments in your browser.
Library on Multi-armed bandit
Contextual Bandit Engine
Code examples for simple reinforcement learning projects
MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent
Variety of Multi-Arm Bandit (MAB) algorithms using classic and advanced strategies, including tools for experiments and simulations in stationary and nonstationary environments
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