This repository is an implementation of the simple bandit algorithm, as described in the book by Barto and Sutton. The algorithm serves as a fundamental exploration-exploitation strategy commonly used ...
This project implements the ε-greedy algorithm to solve the Multi-Armed Bandit (MAB) problem, which is a classic reinforcement learning scenario. The primary challenge in this problem is ...
How does a gambler maximize winnings from a row of slot machines? This is the inspiration for the "multi-armed bandit problem," a common task in reinforcement learning in which "agents" make choices ...
Abstract: In this paper, we study the adversarial bandit problem with multiple plays. We introduce a highly efficient multiple-play bandit algorithm that achieves the minimax optimal performance with ...
1 Robert Bosch Center for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India 2 Department of Computer Science and Engineering, Indian Institute of ...
How does a gambler maximize winnings from a row of slot machines? This is the inspiration for the "multi-armed bandit problem," a common task in reinforcement learning in which "agents" make choices ...
Abstract: In web-based scenarios, new users and new items frequently join the recommendation system over time without prior events. In addition, users always hold dynamic and diversified preferences.
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