Reinforcement Learning
Reinforcement Learning (RL) is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent takes actions and receives feedback in the form of rewards or penalties. The main goal is to learn a strategy, called a policy, that maximizes the total reward over time.
In RL, learning happens through trial and error. The agent explores different actions and learns which ones lead to better outcomes. Unlike supervised learning, RL does not use labeled input-output pairs. Instead, it relies on feedback from the environment to guide learning.
Key Elements of Reinforcement Learning:
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Agent: The learner or decision-maker.
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Environment: Where the agent performs actions.
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Action: What the agent does.
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Reward: Feedback from the environment.
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State: The current situation of the agent.
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Policy: The strategy the agent follows.
Examples:
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Game Playing: AlphaGo learning to play Go better than humans.
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Robotics: A robot learning to walk or pick up objects.
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Self-driving Cars: Learning to drive by receiving rewards for staying in lanes or avoiding collisions.
Conclusion:
Reinforcement Learning is effective for problems where decisions need to be made sequentially and outcomes are learned through experience.
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