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Hands-On Q-Learning with Python

You're reading from  Hands-On Q-Learning with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345803
Pages 212 pages
Edition 1st Edition
Languages
Author (1):
Nazia Habib Nazia Habib
Profile icon Nazia Habib
Toc

Table of Contents (14) Chapters close

Preface 1. Section 1: Q-Learning: A Roadmap
2. Brushing Up on Reinforcement Learning Concepts 3. Getting Started with the Q-Learning Algorithm 4. Setting Up Your First Environment with OpenAI Gym 5. Teaching a Smartcab to Drive Using Q-Learning 6. Section 2: Building and Optimizing Q-Learning Agents
7. Building Q-Networks with TensorFlow 8. Digging Deeper into Deep Q-Networks with Keras and TensorFlow 9. Section 3: Advanced Q-Learning Challenges with Keras, TensorFlow, and OpenAI Gym
10. Decoupling Exploration and Exploitation in Multi-Armed Bandits 11. Further Q-Learning Research and Future Projects 12. Assessments 13. Other Books You May Enjoy

Chapter 3, Setting Up Your First Environment with OpenAI Gym

  1. You can do this by cloning the source instead of installing the package from pip. Further instructions are available in the Gym documentation.
  2. The term state is commonly used in the terminology of solving Markov decision processes, and the term observation is often used when describing RL environment state spaces. Both terms are equivalent in this context.
  3. Calling env.reset() resets the environment's state and returns the environment's current observation or state variable.
  4. The task will end when the done variable is set to True or the reward is set to 20, depending on your implementation. Both conditions indicate that the task has been solved.
  5. Setting env.s will manually change the state of the environment. This is bad practice when implementing an RL strategy; the state should not be set manually when solving...
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