🏆 Reinforcement Learning (RL)

📘 Definition: The model learns by trial and error using rewards and penalties.

🎯 Goal: Maximize cumulative reward through interactions with an environment.

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🧩 Analogy

🐶 Think of it like training a pet. If it sits on command, you give a treat (reward). If it doesn’t, no treat (or a firm “no”). Over time, it learns which behaviors get the reward.

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📘 Official Definition

Reinforcement Learning is an area of machine learning concerned with how agents ought to take actions in an environment to maximize cumulative reward through interactions and feedback.

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🧠 Simple Explanation

Reinforcement Learning (RL) is like learning by trial and error — the system learns by doing something, getting feedback (reward or punishment), and improving over time.

It’s used when decisions need to be made step by step, and each decision affects the next.

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✅ Real-World Examples

• Game AI: Learning how to play and win games(e.g., Chess, Go, Atari)
• Robotics: Teaching a robot to walk or pick up objects.
• Autonomous Driving: Learning how to navigate streets safely.
• Recommendation Systems: Improving content suggestions based on user behavior.

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🔍 Popular Algorithms:

• Q-Learning
• Deep Q-Networks (DQN)
• Policy Gradient Methods

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Sources:

• DeepMind RL Overview
• Wikipedia - Reinforcement Learning
• IBM Cloud Learn - Reinforcement Learning