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Python Machine Learning By Example

You're reading from   Python Machine Learning By Example Unlock machine learning best practices with real-world use cases

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Product type Paperback
Published in Jul 2024
Publisher Packt
ISBN-13 9781835085622
Length 518 pages
Edition 4th Edition
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Author (1):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
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Table of Contents (18) Chapters Close

Preface 1. Getting Started with Machine Learning and Python 2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER 3. Predicting Online Ad Click-Through with Tree-Based Algorithms 4. Predicting Online Ad Click-Through with Logistic Regression 5. Predicting Stock Prices with Regression Algorithms 6. Predicting Stock Prices with Artificial Neural Networks 7. Mining the 20 Newsgroups Dataset with Text Analysis Techniques 8. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling 9. Recognizing Faces with Support Vector Machine 10. Machine Learning Best Practices 11. Categorizing Images of Clothing with Convolutional Neural Networks 12. Making Predictions with Sequences Using Recurrent Neural Networks 13. Advancing Language Understanding and Generation with the Transformer Models 14. Building an Image Search Engine Using CLIP: a Multimodal Approach 15. Making Decisions in Complex Environments with Reinforcement Learning 16. Other Books You May Enjoy
17. Index

Solving the Blackjack problem with the Q-learning algorithm

Q-learning is also a model-free learning algorithm. It updates the Q-function for every step in an episode. We will demonstrate how Q-learning is used to solve the Blackjack environment.

Introducing the Q-learning algorithm

Q-learning is an off-policy learning algorithm that optimizes the Q-values based on data generated by a behavior policy. The behavior policy is a greedy policy where it takes actions that achieve the highest returns for given states. The behavior policy generates learning data and the target policy (the policy we attempt to optimize) updates the Q-values based on the following equation:

Here, is the resulting state after taking action a from state s and r is the associated reward. means that the behavior policy generates the highest Q-value given state . Hyperparameters and are the learning rate and discount factor respectively. The Q-learning equation updates the Q-value (estimated...

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