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A subfield of Machine Learning inspired by human behavior, focused on how an agent should take actions in an environment to maximize a notion of cumulative reward. In reinforcement learning, the agent learns through trial and error, receiving feedback in the form of rewards or penalties, and adjusts its strategy (policy) over time to optimize its long-term objectives. RL is commonly used in areas such as robotics, game playing, and autonomous systems.