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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

Summary

In this chapter, we looked beyond DRL and into the realm of AGI, or at least where we hope we are going with AGI. More importantly, though, we looked at what the next phase of DRL is, how we can tackle its current shortcomings, and where it could go next. We looked at meta learning and what it means to learn to learn. Then we covered the excellent learn2learn library and saw how it could be used on a simple deep learning problem and then a more advanced meta-RL problem with MAML. From there, we looked at another new approach to learning using hindsight with HER. From hindsight, we moved to imagination and reasoning and how this could be incorporated into an agent. Then we finished the chapter by looking at I2A—imagination-augmented agents—and how imagination can help fill in the gaps in our knowledge.

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