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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

Exercises

The further we progress in this book, the more valuable and expensive each of these exercises will become. By expensive, we mean the amount of time you need to invest in each will increase. That may mean you are inclined to do fewer exercises, but please continue to try and do two or three exercises on your own:

  1. Revisit TensorSpace Playground and see if you can understand the difference pooling makes in those models. Remember that we avoid the use of pooling in order to avoid losing spatial integrity.
  2. Open Chapter_7_DQN_CNN.py and alter some of the convolutional layer inputs such as the kernel or stride size. See what effect this has on training.
  3. Tune the hyperparameters or create new ones for Chapter_7_DoubleDQN.py.
  4. Tune the hyperparameters or create new ones for Chapter_7_DDQN.py.
  5. Tune the hyperparameters or create new ones for Chapter_7_DoubleDQN_wprority.py.
  6. Convert...
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