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Reinforcement learning is a type of machine learning where an agent learns to behave in an environment, by performing certain actions and receiving rewards or penalties. The goal of the agent is to learn the optimal policy, which is a mapping from states to actions, that maximizes the cumulative reward over time. Reinforcement learning is used in various applications such as game playing, robotics, and recommendation systems. The key components of reinforcement learning are the agent, the environment, the state, the action, the reward function, and the policy. The agent interacts with the environment by observing the state, taking an action, receiving a reward, and updating its policy based on the observed feedback. The agent uses various algorithms such as Q-learning, SARSA, and Deep Reinforcement Learning to learn the optimal policy.
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