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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

These exercises are here for you to use and learn from. Attempt at least 2-3, and the more you do, the easier later chapters will also be:

  1. What is the difference between an online and offline policy agent?
  2. Tune the hyperparameters for any or all of the examples in this chapter, including the new hyperparameter, lambda.
  3. Change the discretization steps in any example that uses discretization and see what effect it has on training.
  4. Use example Chapter_5_3.py, SARSA(0), and adapt it to another Gym environment that uses a continuous observation space and discrete action space.
  5. Use example Chapter_5_4.py, SARSA(λ), and adapt it to another Gym environment that uses a continuous observation space and discrete action space.
  6. There is a hyperparameter shown in the code that is not used. Which parameter is it?
  7. Use example Chapter_5_5.py, SARSA(λ), Lunar Lander and optimize...
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