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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

Challenging the Unity Obstacle Tower Challenge

In late 2018, Unity, with the help of DeepMind, began development of a challenge designed to task researchers in the most challenging areas of DRL. The challenge was developed with Unity as a Gym interface environment and featured a game using a 3D first-person perspective. The 3D perspective is a type of game interface made famous with the likes of games such as Tomb Raider and Resident Evil, to name just a couple of examples. An example of the game interface is shown in the following screenshot:

Example the obstacle tower challenge

The Obstacle Tower Challenge is not only in 3D, but the patterns and materials in the rooms and on the walls change over the levels. This makes vision generalization even more difficult. Furthermore, the challenge poses multiple concurrent steps to complete tasks. That is, each level requires the character...

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