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Explore the 10-Arm Testbed Simulation! 🎲 Utilize Python to test various ε-greedy strategies in a reinforcement learning environment. Visualize and compare agents' performance as they balance exploration and exploitation. Perfect for learners and enthusiasts! 🚀📊
🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-player (MusicalChair, MEGA, rhoRand, MCTop/RandTopM etc).. Available on PyPI: https://pypi.org/project/SMPyBandits/ and documentation on
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.
MetaHierTS is a novel recommendation system algorithm aimed at enhancing user experiences in online marketing. This algorithm focuses on leveraging metadata and similarities between tasks to optimize decision-making in a multi-task Multi-Armed Bandit (MAB) environment.