There are 7 repositories under bandit-algorithms topic.
🔬 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
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
Python implementation of UCB, EXP3 and Epsilon greedy algorithms
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.
Personalized and Interactive Music Recommendation with Bandit approach
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
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.
Personal reimplementation of some ML algorithms for learning purposes
🐍 🔬 Fast Python implementation of various Kullback-Leibler divergences for 1D and 2D parametric distributions. Also provides optimized code for kl-UCB indexes
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.
Research about Causality-based Reinforcement Learning. This repository includes all needed fundamentals, summary of past work and some most recent development
Source code for blog post on Thompson Sampling
Implementation of famous Bandits algortihm: Explore then commit, UCB & Thompson sampling in python.
An list of papers for causal bandit
Non-stationary Bandits and Meta-Learning with a Small Set of Optimal Arms
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB
Implementation for NeurIPS 2020 paper "Locally Differentially Private (Contextual) Bandits Learning" (https://arxiv.org/abs/2006.00701)
C++ implementation of Multi-Armed Bandits (Gaussian and Bernoulli)
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.
Simple Implementations of Bandit Algorithms in python
Python implementation for Reinforcement Learning algorithms -- Bandit algorithms, MDP, Dynamic Programming (value/policy iteration), Model-free Control (off-policy Monte Carlo, Q-learning)
Contextual Bandit algorithms for Warfarin Treatment