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Achieving human-level control in complex environments through deep reinforcement learning typically involves: A) Training an AI agent to surpass human performance using supervised learning techniques. B) Using deep learning models to simulate human decision-making processes. C) Teaching an AI agent to learn optimal actions through trial and error. D) Developing algorithms that mimic human cognition in real-time scenarios.
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