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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

Playing Flappy Bird

Later in this chapter, we'll develop and test an IL algorithm called DAgger on a new environment. The environment named Flappy Bird emulates the famous Flappy Bird game. Here, our mission is to give you the tools needed to implement code using this environment, starting from the explanation of the interface.

Flappy Bird belongs to the PyGame Learning Environment (PLE), a set of environments that mimic the Arcade Learning Environment (ALE) interface. This is similar to the Gym interface, and later we'll see the differences, although it's simple to use.

The goal of Flappy Bird is to make the bird fly through vertical pipes without hitting them. It is controlled by only one action that makes it flap its wings. If it doesn't fly, it progresses in a decreasing trajectory determined by gravity. A screenshot of the environment is shown here:

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