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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 always, the exercises in this section are here to improve your knowledge and understanding of the material. Please attempt to complete 1-3 of these exercises on your own:

  1. What other constants like π could we use Monte Carlo methods to calculate? Think of an experiment to calculate another constant we use.
  2. Open the Chapter_3_1.py sample code and change the value of n, that is, the number of darts dropped. How does that affect the calculated value for π? Use higher or lower values for n.
  3. When we calculated π, we assumed a uniform distribution of darts. However, in the real world, the darts would likely be distributed in a normal or Gaussian manner. How would this affect the Monte Carlo experiment?
  4. Refer to sample Chapter_3_2.py and change the value of n. How does that affect plot generation? Are you able to fix it?
  5. Open Chapter_3_3.py and change the...
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