Название: Reinforcement Learning: Teaching AI to Make Decisions
Автор: Gilbert Gutiérrez
Издательство: Independently published
Серия: AI from Scratch: Step-by-Step Guide to Mastering Artificial Intelligence
Год: 2025
Страниц: 330
Язык: английский
Формат: epub
Размер: 10.1 MB

Master Reinforcement Learning and Build AI That Thinks for Itself!

Reinforcement Learning (RL) is one of the most exciting fields in Artificial Intelligence, enabling machines to learn from experience, make decisions, and optimize outcomes through trial and error. From self-driving cars and robotics to game-playing AI and financial strategies, RL is transforming industries and shaping the future of AI.

Reinforcement Learning: Teaching AI to Make Decisions is the 11th book in the "AI from Scratch" series, designed to take you from beginner to expert with a structured, step-by-step approach. Whether you're an AI enthusiast, data scientist, software engineer, or researcher, this book will help you understand RL fundamentals, implement deep reinforcement learning models, and apply RL to real-world problems.

Unlike traditional Machine Learning, Reinforcement Learning is about sequential decision-making—an AI agent learns to take actions in an environment to maximize long-term rewards. This book takes a structured and hands-on approach, ensuring that each concept is backed by real-world applications and code implementations.

From fundamentals like Markov Decision Processes (MDPs) and Q-Learning to advanced deep RL techniques such as Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Multi-Agent RL, this book covers it all. You’ll gain both theoretical understanding and practical experience, making you well-equipped to implement RL in a variety of domains. We will use Python and the NumPy library to implement Q-Learning.

What You’ll Learn in This Book:

Introduction to Reinforcement Learning – Understand the core concepts, including agents, rewards, states, actions, and environments.
Key RL Algorithms – Master foundational techniques like Q-Learning, Monte Carlo methods, and Temporal Difference (TD) Learning.
Deep Reinforcement Learning – Learn how neural networks enhance RL, explore Deep Q-Networks (DQN), Policy Gradients, Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC).
Hands-on Implementation – Build and train RL models using Python, TensorFlow, PyTorch, OpenAI Gym, and Stable-Baselines3.
Real-World Applications – Apply RL in robotics, gaming (Atari, Chess, Go), finance, self-driving cars, and industrial automation.
Challenges & Future of RL – Explore reward hacking, sample inefficiency, ethical AI, and the role of RL in Artificial General Intelligence (AGI).

Who Is This Book For?

Beginners & AI Enthusiasts – Learn RL from scratch with clear explanations and practical projects.
Machine Learning Engineers & Data Scientists – Advance your AI skills with deep RL techniques and industry applications.
Researchers & Academics – Gain insights into state-of-the-art RL models and emerging trends.
Software Developers & AI Practitioners – Build real-world AI solutions using reinforcement learning.

Why This Book?

Step-by-step tutorials with hands-on coding exercises.
Mathematical foundations explained intuitively.
Real-world case studies from DeepMind, OpenAI, Tesla, and Google.
Practical projects & exercises to reinforce learning.
Future trends & challenges in RL explored in depth.

Whether you’re training an AI agent to play games, optimize business processes, or control robots, this book equips you with the knowledge and tools to implement Reinforcement Learning from scratch and push the boundaries of AI innovation.

Start your RL journey today and build AI that learns, adapts, and makes smarter decisions!