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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 visual state

Up until now, we have observed state as an encoded value or values. These values may have been the cell number in a grid or the x,y location in an area. Either way, these values have been encoded with respect to some reference. In the case of the grid environment, we may use a number to denote the square or a pair of numbers. For x,y coordinates, we still need to denote an origin, and examples of these three types of encoding mechanism are as follows:

Three types of encoding state for an agent

In the preceding diagram, there are three examples of encoding state for an environment. For the first example, which is on the left, we just use a number to represent that state. Moving right to the next grid, the state is now represented as a pair of digits, row by column. On the far right, we can see our old friend the Lunar Lander and how part of its state...

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