In multi-armed bandit problems, KL-UCB is an upper-confidence-bound style algorithm that is asymptotically optimal: its long-run regret matches the best possible bound allowed by the Lai-Robbins lower bound for that problem. This description is adapted from Wikipedia contributors under CC BY-SA 4.0; changes were made. https://creativecommons.org/licenses/by-sa/4.0/
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Wikipedia: Kullback-Leibler Upper Confidence Bound
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In multi-armed bandit problems, KL-UCB (for Kullback-Leibler Upper Confidence Bound) is a UCB-type algorithm that is asymptotically optimal, in the sense that its regret matches the problem-dependent Lai-Robbins lower bound.
Kullback-Leibler Upper Confidence Bound (Wikipedia)
In Group: Reinforcement Learning and Bandit Algorithms, Lead section
In multi-armed bandit problems, KL-UCB
In Field: Algorithms and Complexity Theory, Lead
In multi-armed bandit problems, KL-UCB (for Kullback-Leibler Upper Confidence Bound) is a UCB-type algorithm that is asymptotically optimal, in the sense that its regret matches the problem-dependent Lai-Robbins lower bound.
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