What is reinforcement learning, and how is it different from supervised learning?

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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, observes outcomes, and receives feedback in the form of rewards or penalties. Over time, it learns an optimal strategy, called a policy, to maximize cumulative rewards. RL is widely used in robotics, game playing (like AlphaGo), self-driving cars, and resource optimization. Key elements include: agent, environment, actions, states, rewards, and policy.

In contrast, Supervised Learning relies on a labeled dataset, where each input has a correct output (target). The model learns by minimizing the difference between predictions and known labels, using algorithms like regression, decision trees, or neural networks. It is used in tasks like spam detection, sentiment analysis, or image classification.

🔑 Key Differences:

• Feedback: In supervised learning, feedback comes in the form of correct labels, while in RL it comes as rewards/punishments after actions.

• Learning Process: Supervised learning learns from fixed datasets, whereas RL learns dynamically by trial and error.

• Goal: Supervised learning focuses on accurate predictions, while RL focuses on optimal decision-making strategies.

• Data: RL doesn’t need labeled data; instead, it learns from interaction history.

👉 In short, supervised learning is about learning from examples, while reinforcement learning is about learning from experience to maximize rewards.

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