The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits

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<strong>Bandits</strong> • K arms each arm i – Wins (reward 1) with probability p i – Looses (reward 0) with probability 1- p i • Exploration vs. Exploitation – Exploration is unbiased – Exploitation is biased by exploration only • Regret – Max return – Actual return
Web Example • Some number of ads that can be displayed – Each ad translates to an arm • Each ad can be clicked on by a user – If clicked reward 1 if not reward 0 • Want to have adds clicked as often as possible – This will make the most money
Page 1 and 2: The Epoch-Greedy Algorithm for Cont
Page 3: Overview • Bandit problem • Con
Page 7 and 8: Contextual Bandits • Add Context
Page 9 and 10: Overview • Bandit problem • Con
Page 11 and 12: Exploration • Look at a fixed tim
Page 13 and 14: Creating a Hypotheses • Simple tw
Page 15 and 16: Epoch • Don’t always know the t
Page 17 and 18: Review Using Web Example • Have a
Page 19: Review (Cont.) • Search Query - H

The Epoch-Greedy Algorithm for Contextual Multi-armed Bandits

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