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.