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

Yahoo! news article recommendation system by linUCB

A lightweight python library for bandit algorithms

Bandit algorithms

Python implementation of UCB, EXP3 and Epsilon greedy algorithms

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

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

Deep contextual bandits in PyTorch: Neural Bandits, Neural Linear, and Linear Full Posterior Sampling with comprehensive benchmarking on synthetic and real datasets

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.

Personal reimplementation of some ML algorithms for learning purposes

A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB

Python library of bandits and RL agents in different real-world environments

Pricing and advertising strategy for the e-commerce of an airline company, based on Multi-Armed Bandits (MABs) algorithms and Gaussian Processes. Simulations include non-stationary environments.

Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms

Multi-Objective Multi-Armed Bandit

The official code repo for HyperAgent for neural bandits and GPT-HyperAgent for content moderation.

Implementation for NeurIPS 2020 paper "Locally Differentially Private (Contextual) Bandits Learning" (https://arxiv.org/abs/2006.00701)

DPE code - Code used in "Optimal Algorithms for Multiplayer Multi-Armed Bandits" (AISTATS 2020)

Comparative analysis of Markov decision processes & intelligent agents

Python implementation for Reinforcement Learning algorithms -- Bandit algorithms, MDP, Dynamic Programming (value/policy iteration), Model-free Control (off-policy Monte Carlo, Q-learning)