Multi-armed Bandit
Multi-armed bandit is a type of reinforcement learning problem where an agent has to choose between multiple actions (arms) with unknown reward distributions in order to maximize the total reward over a period of time. The agent has to balance between exploiting the arm with the highest expected reward and exploring other arms to gather more information about their reward distributions. The goal is to find the optimal strategy that maximizes the cumulative reward over time.
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