There are 3 repositories under multi-armed-bandit topic.
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on
Papers about recommendation systems that I am interested in
Simple A/B testing library for Clojure
:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:
Demo project using multi-armed bandit algorithm
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
Python application to setup and run streaming (contextual) bandit experiments.
Simple implementation of the CGP-UCB algorithm.
More about the exploration-exploitation tradeoff with harder bandits
Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions
Offline evaluation of multi-armed bandit algorithms
Software for the experiments reported in the RecSys 2019 paper "Multi-Armed Recommender System Bandit Ensembles"
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
Author's implementation of the paper Correlated Age-of-Information Bandits.
A curated list on papers about combinatorial multi-armed bandit problems.
Multi-armed bandit algorithm with tensorflow and 11 policies
Easily Score & Rank Codable Objects with ML
This repository is for a Decision Making Aarhus University Course assignment, focusing on using Multi-Armed Bandit algorithms, specifically the epsilon-greedy algorithm, for optimizing click-through rates in digital advertising by balancing the exploration of new ads and the exploitation of successful ones.
A short conceptual replication of "Prefrontal cortex as a meta-reinforcement learning system" in Jax.
Implementation of the X-armed Bandits algorithm, as detailed in the paper, "X-armed Bandits", Bubeck et al., 2011.
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
Implementations of the bandit algorithms with unordered and ordered slates that are described in the paper "Non-Stochastic Bandit Slate Problems", by Kale et al. 2010.
En este proyecto de GitHhub podrás encontrar parte del material que utilizo para impartir las clases del módulo introductorio de Reinforcement Learning (Aprendizaje por Refuerzo)
Contextual Multi-Armed Bandit Item/Reward Tracker & Model Trainer
CUNYBot, an AI that plays complete games of Starcraft.
Multi-Armed Bandit method of accurately estimating the largest parameter out of a set of candidates.
Source code for blog post on Thompson Sampling
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
Code template for multi-armed bandit algorithm
Experiments for paper "Online Learning with Costly Features in Non-stationary Environments"