Python implementations of contextual bandits algorithms
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Updated
Jun 18, 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)
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.
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.
This repository contains code for the paper "Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem".
This repository has all the codes and sources of various RL algorithms that I have implemented.
Our project for the "Data Intelligence Applications" exam at Politecnico di Milano. The project was about Social Influence and Pricing techniques applied to networks.
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.
[Book] :- Andrea Lonza - Reinforcement Learning Algorithms with Python_ Learn, understand, and develop smart algorithms for addressing AI challenges-Packt Publishing (2019)
MAB Simulator is a Python package that provides a framework for simulating and comparing multi-armed bandit algorithms.
Library on Multi-armed bandit
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
Contextual Bandit Engine
This program deploys Thompson Bandit algorithm to solve an ad prediction for highest probability of clicking.
Profiling Vehicles for Improved Small Cell Beam-Vehicle Pairing Using Multi-Armed Bandit
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