Multi-Armed Bandits Problem

In probability theory and machine learning, the multi-armed bandit problem (sometimes called the K-[1] or N-armed bandit problem[2]) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice’s properties are only partially known at the time of allocation, and may become better understood as time passes or by allocating resources to the choice.[3][4] This is a classic reinforcement learning problem that exemplifies the exploration–exploitation tradeoff dilemma.

Solutions and Algorithms

• Gittins Index: 202006071552
• Upper Confidence Bound algorithms: 202006071602

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uid: 202006071550 tags: #algorithms

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