Python utilities to compute a lower bound of the expected sample complexity to identify the best arm in a bandit model

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.

Randomized Greedy Learning Under Full-bandit Feedback

Repository contains codes for the course CS780: Deep Reinforcement Learning

Several multi-armed bandit strategies with additional holding option for smoother exploration.

Reinforcement Learning (COMP 579) Project

💫 Fast Julia implementation of various Kullback-Leibler divergences for 1D parametric distributions. 🏋 Also provides optimized code for kl-UCB indexes

a collection of google colab notebooks with educational stuff about bandits and their variations

An illustrative project including some multi-armed bandit algorithms and contextual bandit algorithms

Bandit and Evolutionary Algorithms using Python

🎩🤠Some Bandit Algorithms in Typescript

A open source multi arm bandit framework for optimize your website quickly. You’ll quickly use the benefits of several simple algorithms—including the epsilon-Greedy, Softmax, and Upper Confidence Bound (UCB) algorithms—by working through this framework written in Java, which you can easily adapt for deployment on your own website.

A collection of implementations of the bandit problem.

This repo contains code for multi-armed bandit algorithm testing and local multiplayer competition.

This repository contains the implementation of a wide variety of Reinforcement Learning Projects in different applications of Bandit Algorithms, MDPs, Distributed RL and Deep RL. These projects include university projects and projects implemented due to interest in Reinforcement Learning.

An list of papers for causal bandit

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

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

Simple Implementations of Bandit Algorithms in python