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Hands-On Deep Learning for Games

You're reading from   Hands-On Deep Learning for Games Leverage the power of neural networks and reinforcement learning to build intelligent games

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Product type Paperback
Published in Mar 2019
Publisher Packt
ISBN-13 9781788994071
Length 392 pages
Edition 1st Edition
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Author (1):
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Micheal Lanham Micheal Lanham
Author Profile Icon Micheal Lanham
Micheal Lanham
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Table of Contents (18) Chapters Close

Preface 1. Section 1: The Basics
2. Deep Learning for Games FREE CHAPTER 3. Convolutional and Recurrent Networks 4. GAN for Games 5. Building a Deep Learning Gaming Chatbot 6. Section 2: Deep Reinforcement Learning
7. Introducing DRL 8. Unity ML-Agents 9. Agent and the Environment 10. Understanding PPO 11. Rewards and Reinforcement Learning 12. Imitation and Transfer Learning 13. Building Multi-Agent Environments 14. Section 3: Building Games
15. Debugging/Testing a Game with DRL 16. Obstacle Tower Challenge and Beyond 17. Other Books You May Enjoy

Curiosity Learning

Up until now, we have considered just the extrinsic or external rewards an agent may receive in an environment. The Hallway example, for instance, gives a +1 external reward when the agent reaches the goal, and a -1 external reward if it gets the wrong goal. However, real animals like us can actually learn based on internal motivations, or by using an internal reward function. A great example of this is a baby (a cat, a human, or whatever) that has an obvious natural motivation to be curious through play. The curiosity of playing provides the baby with an internal or intrinsic reward, but the actual act itself gives it a negative external or extrinsic reward. After all, the baby is expending energy, a negative external reward, yet it plays on and on in order to learn more general information about its environment. This, in turn, allows it to explore more of...

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