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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

All about Rainbow DQN

Throughout this book, we have learned how the various threads in Reinforcement Learning (RL) combined to form modern RL and then advanced to Deep Reinforcement Learning (DRL) with the inclusion of Deep Learning (DL). Like most other specialized fields from this convergence, we now see a divergence back to specialized methods for specific classes of environments. We started to see this in the chapters where we covered Policy Gradient (PG) methods and the environments it specialized on are continuous control. The flip side of this is the more typical episodic game environment, which is episodic with some form of discrete control mechanism. These environments typically perform better with DQN but the problem then becomes about DQN. Well, in this chapter, we will look at how smart people solved that by introducing Rainbow DQN.

In this chapter, we will introduce...

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