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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

Understanding policy gradient methods

One thing we need to understand about PG methods is why we need them and what the intuition is behind them. Then, we can cover some of the mathematics very briefly before diving into the code. So, let's cover the motivation behind using PG methods and what they hope to achieve beyond the other previous methods we have looked at. I have summarized the main points of why/what PG methods do and try to solve:

  • Deterministic versus stochastic functions: We often learn early in science and mathematics that many problems require a single or deterministic answer. In the real world, however, we often equate some amount of error to deterministic calculations to quantify their accuracy. This quantification of how accurate a value is can be taken a step further with stochastic or probabilistic methods.

Stochastic methods are often used to quantify...

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