There are 0 repository under mab topic.
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
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
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
Experiment results using MAB algorithms in Yahoo! Front Page Today Module User Click Log dataset
🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"
This project implements famous MAB algorithms and evaluates them on the basis of their performance - EpsilonGreedy, UCB, BetaThompson, LinUCB, LinThompson.
🐯REPLICA of "Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing"
Multi-Armed-Bandit solutions on AWS to deliver Covid-19 test kits efficiently and effectively
A Python library for all popular multi-armed bandit algorithms.
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)
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
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 bandit cache selection