Reinforcement learning is a type of machine learning in which an agent learns to make decisions in an environment by receiving feedback in the form of rewards or punishments. The agent interacts with the environment by taking actions and receiving feedback based on those actions. The goal of reinforcement learning is for the agent to learn a policy, or a set of rules, that maximizes the cumulative reward over time. This type of learning is particularly useful in scenarios where the optimal solution is not known in advance, and the agent must learn through trial and error. Reinforcement learning has been successfully applied to a variety of tasks, including game playing, robotics, and autonomous driving.
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