Download Citation on ResearchGate | On the Many-armed Bandit Problem | Two models for the "many-armed bandit" problem with two distributions are considered. The goal is to determine the best or most profitable outcome through a series of choices. At the beginning of the experiment, when odds and payouts are unknown, the gambler must determine which machine to pull, in which order and how many times. This is the “multi-armed bandit problem.”. The multi-armed bandit problem is a classic reinforcement learning example where we are given a slot machine with n arms (bandits) with each arm having its own rigged probability distribution of success. Pulling any one of the arms gives you a stochastic reward of either R=+1 for success, or R=0 for themusicvaults.net: Anson Wong.

# Many armed bandit problems

CS885 Lecture 8a: Multi-armed bandits, time: 57:15

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