What is reinforcement learning?

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What is Reinforcement Learning (RL)?

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. Instead of being given explicit answers (as in supervised learning), the agent learns through trial and error by receiving rewards or penalties for its actions.

Key Concepts

1. Agent – The learner or decision-maker (e.g., a robot, software bot).

2. Environment – The system the agent interacts with.

3. State – The current situation the agent is in.

4. Action – The choices available to the agent.

5. Reward – Feedback signal from the environment (positive or negative) after an action.

6. Policy – The strategy the agent follows to choose actions.

Goal

The agent’s goal is to learn a policy that maximizes cumulative reward over time, balancing exploration (trying new actions) and exploitation (using known successful actions).

Examples

• Games: AlphaGo learning to play Go better than humans.

• Robotics: A robot learning to walk or pick objects.

• Recommendation Systems: Suggesting personalized content based on user feedback.

• Self-Driving Cars: Learning to make driving decisions safely.

✅ In short: Reinforcement learning is about learning by doing — an agent learns to act in an environment to maximize rewards over time.

Read More :

Difference between supervised deep learning and unsupervised deep learning.

What is transfer learning?

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