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Hands-On Reinforcement Learning for Games

You're reading from Hands-On Reinforcement Learning for Games Implementing self-learning agents in games using artificial intelligence techniques

Product type Paperback

Published in Jan 2020

Publisher Packt

ISBN-13 9781839214936

Length 432 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Artificial Intelligence

Author (1):

Micheal Lanham

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Exploring the Environment

2. Understanding Rewards-Based Learning FREE CHAPTER

3. Dynamic Programming and the Bellman Equation

4. Monte Carlo Methods

5. Temporal Difference Learning

6. Exploring SARSA

7. Section 2: Exploiting the Knowledge

8. Going Deep with DQN

9. Going Deeper with DDQN

10. Policy Gradient Methods

11. Optimizing for Continuous Control

12. All about Rainbow DQN

13. Exploiting ML-Agents

14. DRL Frameworks

15. Section 3: Reward Yourself

16. 3D Worlds

17. From DRL to AGI

18. Other Books You May Enjoy

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Introducing the Monte Carlo method

The Monte Carlo method was so named because of its similarity to gambling or chance. Hence, the method was named after the famous gambling destination at the time. While the method is extremely powerful, it has been used to describe the atom, quantum mechanics, and the quantity of itself. It is only until fairly recently, within the last 20 years, that it has seen widespread acceptance in everything from engineering to financial analysis. The method itself has now become foundational to many aspects of machine learning and is worth further study for anyone in the AI field.

In the next section, we will see how the Monte Carlo method can be used to solve for .

Solving for

The standard introduction...

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Authors (1)

Micheal Lanham

Micheal Lanham

Micheal Lanham is a proven software and tech innovator with 20 years of experience. During that time, he has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries as an R&D developer. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. He was later introduced to Unity and has been an avid developer, consultant, manager, and author of multiple Unity games, graphic projects, and books ever since.

See other products by Micheal Lanham

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