Reinforcement Learning
RL is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward. The agent explores different actions, receives feedback in the form of rewards or penalties, and updates its strategy to improve future performance. It’s like learning through trial and error, where the agent gets better at achieving goals over time.
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