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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 a bit wider in scope in this chapter in hopes you look through several frameworks on your own:

  1. Take some time and look at one of the frameworks listed earlier but not reviewed in this chapter.
  2. Use SimpleRL to solve a grid-world MDP that is different than the one in the example. Be sure to take the time to tune hyperparameters.
  3. Use Google Dopamine to train an agent to play the LunarLander environment. The best choice is likely RainbowDQN or a variation of that.
  4. Use Keras-RL to train an agent to play the Lunar Lander environment; make sure to spend time tuning hyperparameters.
  5. Use RL Lib to train an agent to play the Lunar Lander environment; make sure to spend time tuning hyperparameters.
  6. Modify the Keras-RL example and modify the network structure. Change the number of neurons and layers.
  7. Modify the RL Lib example and change some of...
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