Bandit Methods
Bandit methods are a class of online learning algorithms used in reinforcement learning problems where the goal is to maximize the cumulative reward over a sequence of actions. In bandit problems, the agent is faced with a set of actions, each with an unknown reward distribution. The agent must choose which action to take at each time step, and the goal is to learn the optimal action while maximizing the cumulative reward. Bandit methods use exploration-exploitation trade-offs to balance between trying new actions and exploiting the current best action. These methods are widely used in recommendation systems, online advertising, and clinical trials.
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