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Product type Paperback

Published in Jul 2024

Publisher Packt

ISBN-13 9781835085622

Length 518 pages

Edition 4th Edition

Languages

Python

Tools

PyTorch

Concepts

Computer Vision

Author (1):

Yuxi (Hayden) Liu

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Table of Contents (18) Chapters

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 FrozenLake environment with dynamic programming

We will focus on the policy-based and value-based dynamic programming algorithms in this section. But let’s start by simulating the FrozenLake environment. It simulates a simple grid-world scenario where an agent navigates through a grid of icy terrain, represented as a frozen lake, to reach a goal tile.

Simulating the FrozenLake environment

FrozenLake is a typical OpenAI Gym (now Gymnasium) environment with discrete states. It is about moving the agent from the starting tile to the destination tile in a grid, and at the same time avoiding traps. The grid is either 4 * 4 (FrozenLake-v1), or 8 * 8 (FrozenLake8x8-v1). There are four types of tiles in the grid:

The starting tile: This is state 0, and it comes with 0 reward.
The goal tile: It is state 15 in the 4 * 4 grid. It gives +1 reward and terminates an episode.
The frozen tile: In the 4 * 4 grid, states 1, 2, 3, 4, 6, 8, 9, 10, 13...

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

Table of Contents (18) Chapters

Solving the FrozenLake environment with dynamic programming

Simulating the FrozenLake environment

Authors (1)

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

Table of Contents (18) Chapters

Solving the FrozenLake environment with dynamic programming

Simulating the FrozenLake environment

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Authors (1)

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