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

A beer recommendation system using multi-armed bandit approach to solve cold start problems

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 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.

This repository contains code for the paper "Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem".

The iRec official command line interface

A Comparative Evaluation of Active Learning Methods in Deep Recommendation

This repository has all the codes and sources of various RL algorithms that I have implemented.

An introduction to multi arm bandits

Our project for the "Data Intelligence Applications" exam at Politecnico di Milano. The project was about Social Influence and Pricing techniques applied to networks.