Python implementations of contextual bandits algorithms
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Updated
Sep 16, 2024 - Python
Python implementations of contextual bandits algorithms
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
implement basic and contextual MAB algorithms for recommendation system
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Interactive Recommender Systems Framework
[Book] :- Andrea Lonza - Reinforcement Learning Algorithms with Python_ Learn, understand, and develop smart algorithms for addressing AI challenges-Packt Publishing (2019)
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
how to deal with multi-armed bandit problem through different approaches
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…
[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.
Create a platform that recommends sustainable farming practices to farmers based on their specific location, soil type, crop choice, and climate conditions. Incorporating data on sustainable agriculture methods could help in increasing crop yield, reducing environmental impact, and promoting biodiversity.
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
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
Implementation of the Adaptive Contextual Combinatorial Upper Confidence Bound (ACC-UCB) algorithm for the contextual combinatorial volatile multi-armed bandit setting.
The iRec official command line interface
Profiling Vehicles for Improved Small Cell Beam-Vehicle Pairing Using Multi-Armed Bandit
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