150 results for “topic:multi-armed-bandit”
Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog
🔬 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
Papers about recommendation systems that I am interested in
A simple, extensible library for developing AutoML systems
Simple A/B testing library for Clojure
:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:
Demo project using multi-armed bandit algorithm
Python application to setup and run streaming (contextual) bandit experiments.
A multi-armed bandit library for Python
Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
Library for multi-armed bandit selection strategies, including efficient deterministic implementations of Thompson sampling and epsilon-greedy.
Python library for Multi-Armed Bandits
Simple implementation of the CGP-UCB algorithm.
R package for Multi-Armed Bandit Simulation Study
More about the exploration-exploitation tradeoff with harder bandits
Offline evaluation of multi-armed bandit algorithms
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
Contextual Multi-Armed Bandit Platform for Scoring, Ranking & Decisions
A short conceptual replication of "Prefrontal cortex as a meta-reinforcement learning system" in Jax.
A curated list on papers about combinatorial multi-armed bandit problems.
Multi-armed bandit algorithm with tensorflow and 11 policies
secondary development by torchsharp for Deep Learning and Reinforcement Learning
Software for the experiments reported in the RecSys 2019 paper "Multi-Armed Recommender System Bandit Ensembles"
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
Easily Score & Rank Codable Objects with ML
Author's implementation of the paper Correlated Age-of-Information Bandits.
Implementation of the X-armed Bandits algorithm, as detailed in the paper, "X-armed Bandits", Bubeck et al., 2011.
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
En este proyecto de GitHhub podrás encontrar parte del material que utilizo para impartir las clases del módulo introductorio de Reinforcement Learning (Aprendizaje por Refuerzo)
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.