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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

Optimizing for Continuous Control

Up until now, we have considered most of the training/challenge environments we've looked at as being episodic; that is, the game or environment has a beginning and an end. This is good since most games have a beginning and an end it is, after all, a game. However, in the real world, or for some games, an episode could last days, weeks, months, or even years. For these types of environment, we no longer think of an episode; rather we work with the concept of an environment that requires continuous control. So far, we have looked at a subset of algorithms that can solve this type of problem but they don't do so very well. So, like most things in RL, we have a special class of algorithms devoted to those types of environment, and we'll explore them in this chapter.

In this chapter, we'll look at improving the policy...

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