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What is Confidence Bounds in Exploration-Exploitation about?

This paper presents a method for addressing the exploitation-exploration trade-off using confidence bounds in algorithm design, particularly in the context of the adversarial bandit problem with shifting and associative reinforcement learning. The proposed algorithm, ShiftBand, achieves improved regret bounds compared to previous methods, demonstrating its effectiveness in scenarios with uncertain reward processes. The results indicate that confidence bounds can be a powerful tool for optimizing decision-ma

Author

sharproentgen3

Language

EN