27 results for “topic:mab”
Catmandu - a data processing toolkit
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
:bust_in_silhouette: Multi-Armed Bandit Algorithms Library (MAB) :cop:
Python application to setup and run streaming (contextual) bandit experiments.
VLAN Mac-address Authentication Manager
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
A Julia Package for providing Multi Armed Bandit Experiments
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
Implementation of recommender ( Pytorch & Keras )
The work in paper "A Reinforcement Learning-Based Solution for Intra-Domain Egress Selection" - Duc-Huy LE, Hai Anh TRAN
🐯REPLICA of "Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing"
Experiment results using MAB algorithms in Yahoo! Front Page Today Module User Click Log dataset
This project implements famous MAB algorithms and evaluates them on the basis of their performance - EpsilonGreedy, UCB, BetaThompson, LinUCB, LinThompson.
Typescript implementation of a multi-armed bandit
A Python library for all popular multi-armed bandit algorithms.
Implementation of the Multi-Armed Bandit where each arm returns continuous numerical rewards. Covers Epsilon-Greedy, UCB1, and Thompson Sampling with detailed explanations.
Multi-Armed-Bandit solutions on AWS to deliver Covid-19 test kits efficiently and effectively
멀티 암드 밴딧 기반 음악 장르 추천 프로그램
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
Algorithms for multi-armed bandit (MAB) problems
My Little Reinforcement Learning
Exploitation vs Exploration problem stated as A/B-testing with maximum profit per unit time.
Source code for Assignment 2 of COMP90051 (Semester 2 2020)
Intelligent pairwise comparisons. Better rankings with fewer votes.
Implementation of Multi-Armed Bandit (MAB) algorithms UCB and Epsilon-Greedy. MAB is a class of problems in reinforcement learning where an agent learns to choose actions from a set of arms, each associated with an unknown reward distribution. UCB and Epsilon-Greedy are popular algorithms for solving MAB problems.
Adaptive selection cache with mab