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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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Toc

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

Exercises

As we progressed through this book, the exercises have morphed from learning exercises to almost research efforts, and that is the case in this chapter. Therefore, the exercises in this chapter are meant for the hardcore RL enthusiast and may not be for everyone:

  1. Tune the hyperparameters for one of the sample visual environments in the ML-Agents toolkit.
  2. Modify the visual observation standard encoder found in the ML-Agents toolkit to include additional layers or different kernel filter settings.
  3. Train an agent with nature_cnn or resnet visual encoder networks and compare their performance with earlier examples using the base visual encoder.
  4. Modify the resnet visual encoder to accommodate many more layers or other variations of filter/kernel size.
  5. Download, install, and play the Unity Obstacle Tower Challenge and see how far you can get in the game. As you play, think...
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