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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 exercises in this section are intended to introduce you to Unity ML-Agents in more detail. If your preference is not to use ML-Agents as a training framework, then move on to the next section and the end of this chapter. For those of you still here, ML-Agents on its own is a powerful toolkit for quickly exploring DRL agents. The toolkit hides most of the details of DRL but that should not be a problem for you to figure out by now:

  1. Set up and run one of the Unity ML-Agents sample environments in the editor to train an agent. This will require that you consult the Unity ML-Agents documentation.
  2. Tune the hyperparameters of a sample Unity environment.
  3. Start TensorBoard and run it so that it collects logs from the Unity runs folder. This will allow you to watch the training performance of the agents being trained with ML-Agents.
  1. Build a Unity environment and train...
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