Python implementations of contextual bandits algorithms
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Updated
Feb 22, 2026 - 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.
Python library for Multi-Armed Bandits
implement basic and contextual MAB algorithms for recommendation system
Interactive Recommender Systems Framework
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…
Implementation of the Adaptive Contextual Combinatorial Upper Confidence Bound (ACC-UCB) algorithm for the contextual combinatorial volatile multi-armed bandit setting.
how to deal with multi-armed bandit problem through different approaches
Deep contextual bandits in PyTorch: Neural Bandits, Neural Linear, and Linear Full Posterior Sampling with comprehensive benchmarking on synthetic and real datasets
A beer recommendation system using multi-armed bandit approach to solve cold start problems
Batched Multi-armed Bandits Problem - Analisi critica. Artificial Intelligence Course Project on the study and experimental results' analysis of a scientific paper.
[Book] :- Andrea Lonza - Reinforcement Learning Algorithms with Python_ Learn, understand, and develop smart algorithms for addressing AI challenges-Packt Publishing (2019)
Source code for blog post on Thompson Sampling
Library on Multi-armed bandit
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.
MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent
The iRec official command line interface
A Comparative Evaluation of Active Learning Methods in Deep Recommendation
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