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Features & Elements of Reinforcement Learning (RL) Explained | Agent, Environment & Learning Process

In this video, we explain the Features and Core Elements of Reinforcement Learning (RL) in a clear and intuitive way. This topic is fundamental for understanding how RL systems learn through interaction and feedback.

🔍 Topics Covered:

Key features of Reinforcement Learning

Core elements of RL

Agent

Environment

State

Action

Reward

How these elements work together in the RL learning loop

Why RL is different from supervised and unsupervised learning

This explanation is exam-oriented, concept-focused, and suitable for engineering students and AI/ML learners.

🎯 Who should watch this?

B.Tech / M.Tech students

Reinforcement Learning beginners

AI / ML enthusiasts

Anyone preparing for RL & Deep Learning exams

🧠 How this video was created

This video was created using:

My college notes

Standard academic textbooks

NotebookLM as a learning and content-structuring assistant

The final explanation and narration are presented in my own words for educational purposes.

📚 References & Learning Resources

The concepts explained are inspired by the following standard resources:

Sutton & Barto – Reinforcement Learning: An Introduction (2nd Edition)
https://web.stanford.edu/class/psych2...

Ian Goodfellow et al. – Deep Learning
https://aikosh.indiaai.gov.in/static/...

College lecture notes

🚀 What’s Next in This Playlist?

Markov Decision Process (MDP)

Policy and Value Functions

Bellman Equations

Exploration vs Exploitation

Q-Learning vs SARSA

📌 Disclaimer

This video is created strictly for educational purposes.
All explanations are original, and no copyrighted material is reproduced.

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If you want next:

🔥 Exploration vs Exploitation description

🎬 MDP video script

📋 Full RL playlist descriptions

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