🔬 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.

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.

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

🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"

Personalized and Interactive Music Recommendation with Bandit approach

Personal reimplementation of some ML algorithms for learning purposes

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.

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.