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Reinforcement Learning Algorithms with Python

You're reading from   Reinforcement Learning Algorithms with Python Learn, understand, and develop smart algorithms for addressing AI challenges

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Product type Paperback
Published in Oct 2019
Publisher Packt
ISBN-13 9781789131116
Length 366 pages
Edition 1st Edition
Languages
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Author (1):
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Andrea Lonza Andrea Lonza
Author Profile Icon Andrea Lonza
Andrea Lonza
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Algorithms and Environments
2. The Landscape of Reinforcement Learning FREE CHAPTER 3. Implementing RL Cycle and OpenAI Gym 4. Solving Problems with Dynamic Programming 5. Section 2: Model-Free RL Algorithms
6. Q-Learning and SARSA Applications 7. Deep Q-Network 8. Learning Stochastic and PG Optimization 9. TRPO and PPO Implementation 10. DDPG and TD3 Applications 11. Section 3: Beyond Model-Free Algorithms and Improvements
12. Model-Based RL 13. Imitation Learning with the DAgger Algorithm 14. Understanding Black-Box Optimization Algorithms 15. Developing the ESBAS Algorithm 16. Practical Implementation for Resolving RL Challenges 17. Assessments
18. Other Books You May Enjoy

Exploration versus exploitation

The exploration-exploitation trade-off dilemma, or exploration-exploitation problem, affects many important domains. Indeed, it's not only restricted to the RL context, but applies to everyday life. The idea behind this dilemma is to establish whether it is better to take the optimal solution that is known so far, or if it's worth trying something new. Let's say you are buying a new book. You could either choose a title from your favorite author, or buy a book of the same genre that Amazon is suggesting to you. In the first case, you are confident about what you're getting, but by selecting the second option, you don't know what to expect. However, in the latter case, you could be incredibly pleased, and end up reading a very good book that is indeed better than the one written by your favorite author.

This conflict between...

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