Multi-Armed Bandit Problems
Multi-Armed Bandit Problems refer to a class of decision-making problems in which an agent must choose between multiple actions, each with an unknown reward distribution. The agent's goal is to maximize its cumulative reward over a sequence of actions. The term 'bandit' refers to the idea that the agent is faced with a set of slot machines (or 'one-armed bandits') and must decide which one to play in order to maximize its winnings. Multi-Armed Bandit Problems are commonly used in fields such as online advertising, clinical trials, and recommender systems, where the agent must balance exploration (trying out new actions to learn their reward distributions) with exploitation (choosing actions that are known to have high rewards based on past experience). There are various algorithms that have been developed to solve Multi-Armed Bandit Problems, including epsilon-greedy, UCB (Upper Confidence Bound), and Thompson Sampling.
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