How can reinforcement learning tackle real-world control problems?
Reinforcement learning offers a powerful framework for tackling real-world control problems where traditional methods may struggle. By defining appropriate reward functions and environments, RL agents can learn to manage complex systems, from optimizing energy grids to controlling robotic manipulators, by iteratively improving their decision-making processes through experience and feedback.
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