The Epsilon-Greedy /UCB ("upper confidence bound") for MAB (Multiarmed-bandit) problem som
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Epsilon-Greedy
supposed an k arm(slot) and set ε a little number between [0,0.1]
In short, epsilon-greedy means pick the current best option ("greedy") most of the time----(1-ε) + ε/k
but pick a random option with a small probability sometimes for other option-----(k-1)ε/k
often works as well as, or even better than, more sophisticated algorithms such as UCB
for more information about
A/B testing
Thompson sampling
see
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