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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

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781839214936
Length 432 pages
Edition 1st Edition
Languages
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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 (19) Chapters Close

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

Creating a new environment

The great thing about the ML-Agents toolkit is the ability it provides for creating new agent environments quickly and simply. You can even transform existing games or game projects into training environments for a range of purposes, from building full robotic simulations to simple game agents or even game agents that play as non-player characters. There is even potential to use DRL agents for game quality assurance testing. Imagine building an army of game testers that learn to play your game with just trial and error. The possibilities are endless and Unity is even building a full cloud-based simulation environment for running or training these agents in the future.

In this section, we will walk through using a game project as a new training environment. Any environment you create in Unity would be best tested with the ML-Agents toolkit before you...

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