Learning to Learn: Reinforcement Learning Explained for Humans
Learning to Learn: Reinforcement Learning Explained for Humans
About the Book
Ever wondered how machines learn to make decisions: not by rules, but by trial and error?
Learning to Learn: Reinforcement Learning Explained for Humans is your doorway into one of the most exciting areas of Artificial Intelligence. Written with stories, analogies, and real Python code, this book transforms complex equations into ideas you’ll never forget.
Inside, you’ll discover:
- The core building blocks of Reinforcement Learning: agents, states, actions, rewards, and policies.
- Why trial-and-error learning powers robots, self-driving cars, recommender systems, and even healthcare AI.
- Intuitive analogies: from curious cats to game-playing algorithms.
- Step-by-step Python examples you can run and modify yourself.
- “Satyam’s Explanation” sections at the end of each chapter that strip away jargon and give you the heart of the idea in plain language.
Whether you’re a student, developer, researcher, or a curious learner, this book is designed to help you not just understand RL, but feel how it works. Each chapter includes quizzes, reflective exercises, and code experiments so you can learn actively.
If you’ve been intimidated by dense math and Bellman equations, this book is the friendly guide you’ve been looking for.
Learn to think like an agent. Learn to learn.
About the Author
Table of Contents
- The Curious Cat: What is Reinforcement Learning?
- Rewards, Not Rules
- The State of Things
- Making Good Decisions: Policies
- Time Travelers: The Value of Future
- The Policy Maker: How Agents Choose What To Do
- The Backbone of RL: Understanding the Bellman Equation
- The Q in Q-Learning
- Exploration vs Exploitation: The Agent's Dilemma
- Deep Reinforcement Learning
- When Rewards Lie: Sparse and Delayed Feedback
- Real-World RL: Robotics, Games, Recommenders
- Where To Go Next?
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