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.

VLAN Mac-address Authentication Manager

Multi-Player Bandits Revisited [L. Besson & É. Kaufmann]

The work in paper "A Reinforcement Learning-Based Solution for Intra-Domain Egress Selection" - Duc-Huy LE, Hai Anh TRAN

My Little Reinforcement Learning

🐯REPLICA of "Auction-based combinatorial multi-armed bandit mechanisms with strategic arms"

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.