#
multi-armed-bandit
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Contextual Bandits in R - simulation and evaluation of Multi-Armed Bandit Policies
machine-learning
cran
statistics
reinforcement-learning
simulation
evaluation
exploration
exploitation
bandit-learning
reinforcement
multi-armed-bandits
multi-armed-bandit
bandit
contextual-bandits
contextual
cmab
multi-armed
bandit-experiments
contextual-bandit-policies
offline-bandit
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Updated
Jul 25, 2020 - R
This repository is for a Decision Making Aarhus University Course assignment, focusing on using Multi-Armed Bandit algorithms, specifically the epsilon-greedy algorithm, for optimizing click-through rates in digital advertising by balancing the exploration of new ads and the exploitation of successful ones.
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Updated
Mar 10, 2024 - R
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