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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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Toc

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

Summary

Hopefully, in this chapter, you have learned about all the tools and components needed to build RL algorithms. You set up the Python environment required to develop RL algorithms and programmed your first algorithm using an OpenAI Gym environment. As the majority of state-of-the-art RL algorithms involve deep learning, you have been introduced to TensorFlow, a deep learning framework that you'll use throughout the book. The use of TensorFlow speeds up the development of deep RL algorithms as it deals with complex parts of deep neural networks such as backpropagation. Furthermore, TensorFlow is provided with TensorBoard, a visualization tool that is used to monitor and help the algorithm debugging process.

Because we'll be using many environments in the subsequent chapters, it's important to have a clear understanding of their differences and distinctiveness...

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