Algorithms for Infinitely Many-Armed Bandits

We consider multi-armed bandit problems where the number of arms is larger than the possible number of experiments. We make a stochastic assumption on the mean-reward of a new selected arm which characterizes its probability of being a near-optimal arm. Our assumption is weaker than in previous works. We describe algorithms based on upper-confidence-bounds applied to a restricted set of randomly selected arms and provide upper-bounds on the resulting expected regret. We also derive a lower-bound which matches (up to a logarithmic factor) the upper-bound in some cases.

• Type: Conference
• Year: 2008
• Where: NIPS
• Authors: Yizao Wang, Jean-Yves Audibert, Rémi Munos
• Keywords: Information Technology, Multi-armed Bandit Problems, Near-optimal Arm, NIPS 2008, Stochastic Assumption