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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

Completing the exercises in this section is entirely optional, but, hopefully, you can start to appreciate that we, as reinforcement learners ourselves, learn best by doing. Do your best and attempt to complete at least 2-3 exercises from the following:

  1. Consider other problems you could use DP with? How would you break the problem up into subproblems and calculate each subproblem?
  2. Code up another example that compares a problem programmed linearly versus dynamically. Use the example from Exercise 1. The code examples, Chapter_2_2.py and Chapter_2_3.py, are good examples of side-by-side comparisons.
  3. Look through the OpenAI documentation and explore other RL environments.
  4. Create, render, and explore other RL environments from Gym using the sample test code from Chapter_2_4.py.
  5. Explain the process/algorithm of evaluating and improving a policy using DP.
  6. Explain the difference...
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