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

Summary

In this chapter, we quickly skimmed through the very interesting domain of continuous control using RL methods, and we checked three different algorithms on one problem of a four-legged robot. In our training, we used an emulator, but there are real models of this robot made by the Ghost Robotics company. (You can check out the cool video on YouTube: https://youtu.be/bnKOeMoibLg.) We applied three training methods to this environment: A2C, DDPG, and D4PG (which showed the best results).

In the next chapter, we will continue exploring the continuous action domain and check a different set of improvements: trust region extension.

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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

Summary

Unlock this book and the full library FREE for 7 days

Author (1)

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