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Hands-On Reinforcement Learning for Games

You're reading from Hands-On Reinforcement Learning for Games

Product type Book

Published in Jan 2020

Publisher Packt

ISBN-13 9781839214936

Pages 432 pages

Edition 1st Edition

Languages

Concepts

Game Artificial Intelligence

Author (1):

Micheal Lanham

Table of Contents (19) Chapters

Preface

1. Section 1: Exploring the Environment

2. Understanding Rewards-Based Learning

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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Exercises

As always, the exercises in this section are here to improve your knowledge and understanding of the material. Please attempt to complete 1-3 of these exercises on your own:

What other constants like π could we use Monte Carlo methods to calculate? Think of an experiment to calculate another constant we use.
Open the Chapter_3_1.py sample code and change the value of n, that is, the number of darts dropped. How does that affect the calculated value for π? Use higher or lower values for n.
When we calculated π, we assumed a uniform distribution of darts. However, in the real world, the darts would likely be distributed in a normal or Gaussian manner. How would this affect the Monte Carlo experiment?
Refer to sample Chapter_3_2.py and change the value of n. How does that affect plot generation? Are you able to fix it?
Open Chapter_3_3.py and change the...

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Authors (1)

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.

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