You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

The code outline

Okay, now let's switch to the code, which is in directory Chapter24 in the book's repository. In this section, I'm going to give a quick outline of my implementation and the key design decisions, but before that, I have to emphasize the important points about the code to set up the correct expectations:

I'm not a researcher, so the original goal of this code was just to reimplement the paper's method. Unfortunately, the paper has very few details about the exact hyperparameters used, so I had to guess and experiment a lot, and still, my results are very different from those published in the paper.
At the same time, I've tried to implement everything in a general way to simplify further experiments. For example, the exact details about the cube state and transformations are abstracted away, which allows us to implement more puzzles similar to the 3×3 cube just by adding a new module. In my code, two cubes are implemented...

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

The code outline

Unlock this book and the full library FREE for 7 days

Author (1)

TensorFlow Reinforcement Learning Quick Start Guide

Deep Reinforcement Learning with Python

Hands-On Intelligent Agents with OpenAI Gym

Hands-On Q-Learning with Python

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