Reinforcement Learning Applications
Reinforcement Learning (RL) trains models to make decisions by rewarding desirable actions and penalizing undesired ones. It is widely used in robotics, game development, and autonomous systems. RL algorithms excel in environments where the best course of action is not immediately obvious. Applications include optimizing supply chain operations and creating adaptive learning systems. RL's iterative approach enables systems to continuously improve performance over time.
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