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You can use this page to email Satyam Mishra about 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
If AI had a dating profile, Satyam Mishra would probably be the person it swipes right on. With 5+ years of wrangling code, math, and GPUs (sometimes successfully), he’s been building everything from GPT-powered autonomous agents to robots that can navigate the world without bumping into walls… most of the time.
Specialties? Oh, just the casual stuff: Generative AI, Machine Learning, Computer Vision, Embedded Systems, and Agentic AI. Recently, he’s been flirting with LLM optimization, Koopman-based AI (yes, it’s as fancy as it sounds), optical computing (optical neural networks), and making AI explain why it does what it does without sounding like a politician.
Projects? He’s rolled out grammar-constrained reinforcement learning (because even AI needs to mind its language), fine-tuned LLMs with RAG systems, built contactless facial authentication (so you can look at your computer and it just knows), and cooked up personalized LMS/ERP platforms that make both students and managers slightly less stressed.
He’s also been the brains behind KoopFormer (a physics-inspired transformer that teaches AI motion synthesis, still in progress work) and NeuroExplain (an LLM that talks neuroscience without melting your brain, still in progress work). On the ops side, he’s deployed secure hybrid AI platforms with K3s, Vault, Prometheus, and GitOps: basically making AI run like a well-oiled (and well-guarded) machine.