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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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Toc

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

Exploring Habitat – embodied agents by FAIR

Habitat is a relatively new entry by Facebook AI Research for a new form of embodied agents. This platform represents the ability to represent full 3D worlds displayed from real-world complex scenes. The environment is intended for AI research of robots and robotic-like applications that DRL will likely power in the coming years. To be fair though, pun intended, this environment is implemented to train all forms of AI on this type of environment. The current Habitat repository only features some simple examples and implementation of PPO.

The Habitat platform comes in two pieces: the Habitat Sim and Habitat API. The simulation environment is a full 3D powered world that can render at thousands of frames per second, which is powered by photogrammetry RGBD data. RGBD is essentially RGB color data plus depth. Therefore, any image...

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