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.
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Simple implementation of the CGP-UCB algorithm.
Offline evaluation of multi-armed bandit algorithms
More about the exploration-exploitation tradeoff with harder bandits
Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
A curated list on papers about combinatorial multi-armed bandit problems.
Multi-armed bandit algorithm with tensorflow and 11 policies
A short conceptual replication of "Prefrontal cortex as a meta-reinforcement learning system" in Jax.
Software for the experiments reported in the RecSys 2019 paper "Multi-Armed Recommender System Bandit Ensembles"
Author's implementation of the paper Correlated Age-of-Information Bandits.
Easily Score & Rank Codable Objects with ML
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
A comprehensive Python library implementing a variety of contextual and non-contextual multi-armed bandit algorithms—including LinUCB, Epsilon-Greedy, Upper Confidence Bound (UCB), Thompson Sampling, KernelUCB, NeuralLinearBandit, and DecisionTreeBandit—designed for reinforcement learning applications
Implementation of the X-armed Bandits algorithm, as detailed in the paper, "X-armed Bandits", Bubeck et al., 2011.
Contextual Multi-Armed Bandit Reward Tracker & Model Trainer
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
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)
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.
Solutions for course: "Applied Game Theory" taken at University of Novi Sad - Faculty of Technical Sciences
Multi-Armed Bandit method of accurately estimating the largest parameter out of a set of candidates.
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
CUNYBot, an AI that plays complete games of Starcraft.
A Novel Multi-Arm Bandit Optimization Implementation using reinforcement learning in Python for selecting Notifications.