The multi-armed bandit (MAB) metaphor encapsulates the challenge of sequential decision-making under uncertainty, where a decision-maker repeatedly chooses among competing options (or “arms”) and ...
How does a gambler maximize winnings from a row of slot machines? This is the inspiration for the "multi-armed bandit problem," a common task in reinforcement learning in which "agents" make choices ...
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