The Multi-armed Bandit Problem is a classic problem in decision theory and reinforcement learning that explores the trade-off between exploration and exploitation to maximize rewards. It models scenarios where you must choose between multiple options with uncertain payoffs, akin to selecting which arm of a slot machine to pull to achieve the highest cumulative reward over time.