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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 FREE CHAPTER
2. The Landscape of Reinforcement Learning 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

Technical requirements

After a brief theoretical introduction to grasp the core concepts behind the imitation learning algorithms, we'll implement a real IL algorithm. However, we'll provide only the main and most interesting parts. Thus, if you are interested in the full implementation, you can find it in the GitHub repository of this book: https://github.com/PacktPublishing/Reinforcement-Learning-Algorithms-with-Python.

Installation of Flappy Bird

Later, we'll run our IL algorithm on a revisited version of a famous game called Flappy Bird (https://en.wikipedia.org/wiki/Flappy_Bird). In this section, we'll give you all the commands needed to install it.

But before installing the environment of the game...

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