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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

Generalizing 3D vision

As previously mentioned in Chapter 11, Exploiting ML-Agents, we saw how the team at Unity is one of the leaders in training agents for 3D worlds. After all, they do have a strong vested interest in providing an AI platform that developers can just plug into and build intelligent agents. Except, the very agents that fit this broad type of application are now considered the first step to AGI because if Unity can successfully build a universal agent to play any game, it will have effectively built a first-level AGI.

The problem with defining AGI is trying to understand how broad or general an intelligence has to be as well as how we quantify the agent's understanding of that environment and possible ability to transfer knowledge to other tasks. We really won't know how best to define what that is until someone has the confidence to stand up and claim...

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