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Mastering Reinforcement Learning with Python

You're reading from   Mastering Reinforcement Learning with Python Build next-generation, self-learning models using reinforcement learning techniques and best practices

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Product type Paperback
Published in Dec 2020
Publisher Packt
ISBN-13 9781838644147
Length 544 pages
Edition 1st Edition
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Author (1):
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Enes Bilgin Enes Bilgin
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Enes Bilgin
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Table of Contents (24) Chapters Close

Preface 1. Section 1: Reinforcement Learning Foundations
2. Chapter 1: Introduction to Reinforcement Learning FREE CHAPTER 3. Chapter 2: Multi-Armed Bandits 4. Chapter 3: Contextual Bandits 5. Chapter 4: Makings of a Markov Decision Process 6. Chapter 5: Solving the Reinforcement Learning Problem 7. Section 2: Deep Reinforcement Learning
8. Chapter 6: Deep Q-Learning at Scale 9. Chapter 7: Policy-Based Methods 10. Chapter 8: Model-Based Methods 11. Chapter 9: Multi-Agent Reinforcement Learning 12. Section 3: Advanced Topics in RL
13. Chapter 10: Introducing Machine Teaching 14. Chapter 11: Achieving Generalization and Overcoming Partial Observability 15. Chapter 12: Meta-Reinforcement Learning 16. Chapter 13: Exploring Advanced Topics 17. Section 4: Applications of RL
18. Chapter 14: Solving Robot Learning 19. Chapter 15: Supply Chain Management 20. Chapter 16: Personalization, Marketing, and Finance 21. Chapter 17: Smart City and Cybersecurity 22. Chapter 18: Challenges and Future Directions in Reinforcement Learning 23. Other Books You May Enjoy

Meta-reinforcement learning with recurrent policies

In this section, we cover one of the more intuitive approaches in meta-reinforcement learning that uses recurrent neural networks to keep a memory, also known as the RL­2 algorithm. Let's start with an example to motivate this approach.

Grid world example

Consider a grid world where the agent's task is to reach a goal state G from a start state S. These states are randomly placed for different tasks, so the agent has to learn exploring the world to discover where the goals state is, which then is given a big reward. When the same task is repeated, the agent is expected to quickly reach the goal state, which is, adapt to the environment, since there is a penalty incurred for each time step. This is described in Figure 12.1.

Figure 12.1 – Grid world example for meta-RL. (a) A task, (b) agent's exploration of the task, (c) agent's exploitation of what it learned.

In order...

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