You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. 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 with simulating the FrozenLake environment.

Simulating the FrozenLake environment

FrozenLake is a typical OpenAI Gym 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 (https://gym.openai.com/envs/FrozenLake-v0/), or 8 * 8 (https://gym.openai.com/envs/FrozenLake8x8-v0/). There are four types of tiles in the grid:

S: The starting tile. This is state 0, and it comes with 0 reward.
G: The goal tile. It is state 15 in the 4 * 4 grid. It gives +1 reward and terminates an episode.
F: The frozen tile. In the 4 * 4 grid, states 1, 2, 3, 4, 6, 8, 9, 10, 13, and 14 are walkable tiles. It gives 0 reward.
H: The hole tile. In the 4 * 4 grid, states 5...