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Artificial Intelligence with Python

You're reading from   Artificial Intelligence with Python Your complete guide to building intelligent apps using Python 3.x

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839219535
Length 618 pages
Edition 2nd Edition
Languages
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Authors (2):
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Prateek Joshi Prateek Joshi
Author Profile Icon Prateek Joshi
Prateek Joshi
Alberto Artasanchez Alberto Artasanchez
Author Profile Icon Alberto Artasanchez
Alberto Artasanchez
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Toc

Table of Contents (26) Chapters Close

Preface 1. Introduction to Artificial Intelligence 2. Fundamental Use Cases for Artificial Intelligence FREE CHAPTER 3. Machine Learning Pipelines 4. Feature Selection and Feature Engineering 5. Classification and Regression Using Supervised Learning 6. Predictive Analytics with Ensemble Learning 7. Detecting Patterns with Unsupervised Learning 8. Building Recommender Systems 9. Logic Programming 10. Heuristic Search Techniques 11. Genetic Algorithms and Genetic Programming 12. Artificial Intelligence on the Cloud 13. Building Games with Artificial Intelligence 14. Building a Speech Recognizer 15. Natural Language Processing 16. Chatbots 17. Sequential Data and Time Series Analysis 18. Image Recognition 19. Neural Networks 20. Deep Learning with Convolutional Neural Networks 21. Recurrent Neural Networks and Other Deep Learning Models 22. Creating Intelligent Agents with Reinforcement Learning 23. Artificial Intelligence and Big Data 24. Other Books You May Enjoy
25. Index

Creating Intelligent Agents with Reinforcement Learning

In this chapter, we are going to learn about reinforcement learning (RL). We will discuss the premise of RL. We will talk about the differences between RL and supervised learning. We will go through some real-world examples of RL and see how it manifests itself in various forms. We will learn about the building blocks of RL and the various concepts involved. We will then create an environment in Python to see how it works in practice. We will then use these concepts to build a learning agent.

In this chapter, we will cover the following topics:

  • Understanding what it means to learn
  • Reinforcement learning versus supervised learning
  • Real-world examples of RL
  • Building blocks of RL
  • Creating an environment
  • Building a learning agent

Before we move into RL itself, let's first think about what it actually means to learn; after all, it will help us to understand it before we go about...

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