Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
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Updated
Dec 11, 2019 - Python
Online Deep Learning: Learning Deep Neural Networks on the Fly / Non-linear Contextual Bandit Algorithm (ONN_THS)
👤 Multi-Armed Bandit Algorithms Library (MAB) 👮
Python application to setup and run streaming (contextual) bandit experiments.
COLEMAN (Combinatorial VOlatiLE Multi-Armed BANdit) - and strategies for HCS context
A Julia Package for providing Multi Armed Bandit Experiments
Reinforcement learning techniques applied to solve pricing problems in e-commerce applications. Final project for "Online learning applications" course (2021-2022)
Experiment results using MAB algorithms in Yahoo! Front Page Today Module User Click Log dataset
VLAN Mac-address Authentication Manager
Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]
This project implements famous MAB algorithms and evaluates them on the basis of their performance - EpsilonGreedy, UCB, BetaThompson, LinUCB, LinThompson.
Multi-Armed-Bandit solutions on AWS to deliver Covid-19 test kits efficiently and effectively
My Little Reinforcement Learning
Exploitation vs Exploration problem stated as A/B-testing with maximum profit per unit time.
🐯REPLICA of "Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing"
Typescript implementation of a multi-armed bandit
Adaptive bandit cache selection
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.
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