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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

Introducing DP

DP was developed by Richard E. Bellman in the 1950s as a way to optimize and solve complex decision problems. The method was first applied to engineering control problems but has since found uses in all disciplines requiring the analytical modeling of problems and subproblems. In effect, all DP is about is solving subproblems and then finding relationships to connect those to solve bigger problems. It does all of this by first applying the Bellman optimality equation and then solving it.

Before we get to solving a finite MDP with DP, we will want to understand, in a little more detail, what it is we are talking about. Let's look at a simple example of the difference between normal recursion and DP in the next section.

Regular programming versus DP

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