There are 2 repositories under multiarmed-bandits topic.
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
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 interested in, and surprise them with offers they would have never searched for. In this project work, we explore the use of Reinforcement Learning based techniques to solve the problem of Movie Recommendation. We have implemented the following strategies: Multi Armed Bandits based recommender and an Actor-Critic based recommender framework using Deep Reinforcement Learning.
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
[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".
The iRec official command line interface
A Comparative Evaluation of Active Learning Methods in Deep Recommendation
MABSearch: The Bandit Way of Learning the Learning Rate - A Harmony Between Reinforcement Learning and Gradient Descent
Library on Multi-armed bandit
MAB Simulator is a Python package that provides a framework for simulating and comparing multi-armed bandit algorithms.
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 online learning techniques applied to networks.
Our project for the "Data Intelligence Applications" exam at Politecnico di Milano. The project was about Social Influence and Pricing techniques applied to networks.
This repository has all the codes and sources of various RL algorithms that I have implemented.
This program deploys Thompson Bandit algorithm to solve an ad prediction for highest probability of clicking.
This repository contains the code necessary for generating the figures presented in the paper titled "Cooperative Thresholded Lasso for Sparse Linear Bandit".
Thompson Sampling equipped with Goodness of Fit test based active change-point detection in Non-Stationary Bandit environment
This repository contains hands on code for tutorials on PRICAI 2023 with the topics of Reinforcement Learning for Digital Business
Multi-Stage-Multi-Armed Bandits (MAB) are a class of reinforcement learning problems where an agent tries to maximize its cumulative reward by sequentially selecting actions from multiple options (arms) and observing the rewards associated with those actions.
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.
We show performance of various algorithms in semi-bandit setting and try to solve a real word problem using the same
Sending personalized marketing offers (called free play in a casino setting) to players by observing data on their gaming behavior and demographic information
Repo containing lab files for "Machine Learning" course taken during academic year 2022-2023 summer semester of Master of Telecommunication Engineering program at Politecnico di Milano
Music Recommendation system with a Contextual Multi-Armed Bandit