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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 progress through this book, the exercises at the end of each chapter will be more directed toward providing you with agent training experience. Training RL agents not only requires a fair amount of patience but also intuition on how to spot whether something is wrong or right. That only comes with training experience, so use the following exercises to learn that:

  1. Open example Chapter_4_2.py and change the gridSize variable to see what effect this has on convergence.
  2. Open example Chapter_4_2.py and tune the hyperparameters for alpha and gamma. Try to find the optimum values for both. This will require you to run the example multiple times.
  3. Open example Chapter_4_2.py and change the number of episodes, up or down. See what effect a large number of episodes, such as 100,000 or 1,000,000, has on training.
  4. Tune the learning_rate and gamma hyperparameters in example...
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