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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 exercises in this section are for you to explore on your own. Substantially advancing any of the techniques we cover from this point forward is an accomplishment, so the work you do here could morph into something beyond just learning. Indeed, the environments and examples you work on now will likely indicate your working preference going forward. As always, try to complete two to three of the following exercises:

  1. Tune the hyperparameters for Chapter_9_PPO.py and/or Chapter_9_PPO_LSTM.py.
  2. Tune the hyperparameters for Chapter_9_A2C.py and/or Chapter_9_A3C.py.
  3. Tune the hyperparameters for Chapter_9_ACER.py.
  4. Apply LSTM layers to the A2C and/or A3C examples.
  5. Apply LSTM layers to the ACER example.
  6. Add a play_game function to the A2C and/or A3C examples.
  7. Add a play_game function to the ACER example.
  8. Adjust the buffer size in the ACER example and see how that improves...
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