Python implementations of contextual bandits algorithms
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Updated
Jun 25, 2023 - Python
Python implementations of contextual bandits algorithms
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
implement basic and contextual MAB algorithms for recommendation system
[Book] :- Andrea Lonza - Reinforcement Learning Algorithms with Python_ Learn, understand, and develop smart algorithms for addressing AI challenges-Packt Publishing (2019)
Interactive Recommender Systems Framework
Our project for the "Data Intelligence Applications" exam at Politecnico di Milano. The project was about Social Influence and Pricing techniques applied to networks.
Library on Multi-armed bandit
Recommender Systems are the systems designed to that are designed to recommend things to the user based on many different factors. These systems predict the most likely product that the users are most likely to purchase and are of interest to. Recommendations typically speed up searches and make it easier for users to access content they’re inte…
how to deal with multi-armed bandit problem through different approaches
[Python] Explored different Multiarmed Bandits algorithms to find the best election campaigns more effectively
Learning, Evaluation and Avoidance of Failure situations (LEAF) is a tool to that prevents failures in robot's task plan by learning from previous experience.
A beer recommendation system using multi-armed bandit approach to solve cold start problems
MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent
Implementation of the Adaptive Contextual Combinatorial Upper Confidence Bound (ACC-UCB) algorithm for the contextual combinatorial volatile multi-armed bandit setting.
Source code for blog post on Thompson Sampling
This repository contains hands on code for tutorials on PRICAI 2023 with the topics of Reinforcement Learning for Digital Business
The iRec official command line interface
Profiling Vehicles for Improved Small Cell Beam-Vehicle Pairing Using Multi-Armed Bandit
A benchmark to test decision-making algorithms for contextual-bandits. The library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties.
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