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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

The following is a mix of simple and very difficult exercises. Choose those exercises that you feel appropriate to your interests, abilities, and resources. Some of the exercises in the following list could require considerable resources, so pick those that are within your time/resource budget:

  1. Tune the hyperparameters for sample Chapter_14_learn.py. This sample is a standard deep learning model, but the parameters should be familiar enough to figure out on your own.
  2. Tune the hyperparameters for sample Chapter_14_MetaSGD-VPG.py, as you normally would.
  3. Tune the hyperparameters for sample Chapter_14_Imagination.py. There are a few new hyperparameters in this sample that you should familiarize yourself with.
  4. Tune the hyperparameters for the Chapter_14_wo_HER.py and Chapter_14_HER.py examples. It can be very beneficial for your understanding to train the sample with and...
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