Reinforcement Learning

Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize cumulative rewards. Through trial and error, the agent receives feedback in the form of rewards or penalties, enabling it to learn optimal strategies and policies for complex tasks such as game playing, robotic control, and autonomous driving.

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