Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to achieve some goal. The agent learns from the consequences of its actions, rather than from explicit teaching.
How Reinforcement Learning Functions
In reinforcement learning, the agent receives rewards or penalties based on its actions. The goal is to learn a policy that maximizes the total cumulative reward. It is widely used in areas such as robotics, gaming, and navigation.
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