Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Reinforcement Learning: Teaching AI to Make Decisions

Reinforcement Learning: Teaching AI to Make Decisions

Gutiérrez, Gilbert

Paperback

Series: AI from Scratch: Step-By-Step Guide to Mastering Artificial Intelligence, Book 11

General Computers

ISBN13: 9798310414112
Publisher: Independently Published
Published: Feb 11 2025
Pages: 290
Weight: 1.49
Height: 0.61 Width: 8.50 Depth: 11.00
Language: English
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.

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).

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