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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

Imagination and reasoning in RL

Something that we can observe from our own experience of learning is how imagination can benefit the learning process. Pure imagination is the stuff of deep abstract thoughts and dreams, often closer to a hallucination than any way to solve a real problem. Except, this same imagination can be used to span gaps in our understanding of knowledge and allow us to reason out possible solutions. Say that we are trying to solve the problem of putting a puzzle together, and all we have are three remaining, mostly black pieces, as shown in the following image:

Imagining what the three missing puzzle pieces may look like

Given the simplicity of the preceding diagram, it is quite easy for us to imagine what those puzzle pieces may look like. We are able to fill in those gaps quite easily using our imagination from previous observations and reasoning. This...

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