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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

Understanding DQN in PyTorch

Deep reinforcement learning became prominent because of the work of combining Q-learning with DL. The combination is known as deep Q-learning or DQN for Deep Q Network. This algorithm has powered some of the cutting edge examples of DRL, when Google DeepMind used it to make classic Atari games better than humans in 2012. There are many implementations of this algorithm, and Google has even patented it. The current consensus is that Google patented such a base algorithm in order to thwart patent trolls striking at little guys or developers building commercial applications with DQN. It is unlikely that Google would exercise this legally or that it would have to since this algorithm is no longer considered state of the art.

Patent trolling is a practice whereby an often less-than-ethical company will patent any and all manner of inventions just for the...
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