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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

Testing and results

Let's look at the results we see from running this DQN:

We're definitely making some progress here. We're able to score some points, and the further we go, the higher our score goes, even if the progress is slow. But it still doesn't seem like we're getting consistently closer to solving the task. Our average score isn't climbing high enough to reach the required level.

One issue we might be experiencing is noise in our model. Because there are so many states in our model and so much potential feedback, we might be receiving noisy feedback that's slowing down our model's ability to generalize from the data. Remember that we've chosen a low alpha value to try to cut down on overfitting and too much learning from noise.

What changes can we make now to improve our performance? We can tune the hyperparameters to see...

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