🔬 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

A hyperparameter optimization framework, inspired by Optuna.

PyXAB - A Python Library for X-Armed Bandit and Online Blackbox Optimization Algorithms

Yahoo! news article recommendation system by linUCB

Big Data's open seminars: An Interactive Introduction to Reinforcement Learning

My solutions to Yandex Practical Reinforcement Learning course in PyTorch and Tensorflow

A lightweight python library for bandit algorithms

Bandit algorithms

Python implementation of UCB, EXP3 and Epsilon greedy algorithms

Code for our ACML and INTERSPEECH papers: "Speaker Diarization as a Fully Online Bandit Learning Problem in MiniVox".

More about the exploration-exploitation tradeoff with harder bandits

Privacy-Preserving Bandits (MLSys'20)

A curated list on papers about combinatorial multi-armed bandit problems.

Building recommender Systems using contextual bandit methods to address cold-start issue and online real-time learning

This is a collection of interesting papers that I have read so far or want to read. Note that the list is not up-to-date. Topics: reinforcement learning, deep learning, mathematics, statistics, bandit algorithms, optimization.

🐯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

Personalized and Interactive Music Recommendation with Bandit approach

Personal reimplementation of some ML algorithms for learning purposes

This repository aims at learning most popular MAB and CMAB algorithms and watch how they run. It is interesting for those wishing to start learning these topics.