Multi-armed Bandits The multi-armed bandit problem serves as an introductory concept in reinforcement learning, much like how the house pricing problem acts as a starting point in machine learning. In the K-armed bandit problem, an agent is presented with a set of ‘k’ actions to choose from, and the agent’s decision results in a corresponding reward. Value of an action is the expected or mean reward given when that action is selected.
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