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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

Learning meta learning

The word "meta" is defined as "referring to itself or its type or genre". When talking about meta learning, we are talking about understanding the learning process of learning—that is, instead of thinking about how an agent learns a task, we want to think about how an agent could learn to learn across tasks. It is both an interesting and yet abstract problem, so we first want to explore what meta learning is. In the next section, we will explore how machine learning can learn to learn.

Learning 2 learn

Any good learning model should be trained across a variety of tasks and then generalized to fit the best distribution of those tasks. While we have covered very little with respect...

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