Python implementations of contextual bandits algorithms
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Updated
Oct 31, 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)
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
Interactive Recommender Systems Framework
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
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…
A beer recommendation system using multi-armed bandit approach to solve cold start problems
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.
Batched Multi-armed Bandits Problem - Analisi critica. Artificial Intelligence Course Project on the study and experimental results' analysis of a scientific paper.
Source code for blog post on Thompson Sampling
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.
[Book] :- Andrea Lonza - Reinforcement Learning Algorithms with Python_ Learn, understand, and develop smart algorithms for addressing AI challenges-Packt Publishing (2019)
This repository contains code for the paper "Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem".
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
An introduction to multi arm bandits
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