Multi-Stage-Multi-Armed Bandits (MAB) are a class of reinforcement learning problems where an agent tries to maximize its cumulative reward by sequentially selecting actions from multiple options (arms) and observing the rewards associated with those actions.
This literature review delves into the world of multi-armed bandit problems, exploring their applications and solutions in sequential decision-making scenarios
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