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Hands-On Q-Learning with Python

You're reading from   Hands-On Q-Learning with Python Practical Q-learning with OpenAI Gym, Keras, and TensorFlow

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Product type Paperback
Published in Apr 2019
Publisher Packt
ISBN-13 9781789345803
Length 212 pages
Edition 1st Edition
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Author (1):
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Nazia Habib Nazia Habib
Author Profile Icon Nazia Habib
Nazia Habib
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Table of Contents (14) Chapters Close

Preface 1. Section 1: Q-Learning: A Roadmap FREE CHAPTER
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

Your Q-learning agent in its environment

Let's talk about the self-driving taxi agent that we'll be building. Recall that the Taxi-v2 environment has 500 states, and 6 possible actions that can be taken from each state.

Your objective in the taxi environment is to pick up a passenger at one location, and drop them off at their desired destination in as few timesteps as possible.

You receive points for a successful drop-off, and lose points for the time it takes to complete the task, so your goal is to complete the task in as little time as possible. You also lose points for incorrect actions, such as dropping a passenger off at the wrong location.

Because your goal is to get to both the pickup and drop-off locations as quickly as possible, you lose one point for every move you make per timestep.

Your agent's goal in solving this problem is to find the optimal policy...

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