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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
KerasTensorFlow
Concepts
Deep Reinforcement Learning
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Author (1)
Maxim Lapan
Maxim Lapan
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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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Going hardcore: CuLE

During the writing of this chapter, NVIDIA researchers published the paper and code for their latest experiments with porting the Atari emulator on GPU: Steven Dalton, Iuri Frosio, GPU-Accelerated Atari Emulation for Reinforcement Learning, 2019, arXiv:1907.08467. The code of their Atari port is called CuLE (CUDA Learning Environment) and is available on GitHub: https://github.com/NVlabs/cule.

According to their paper, by keeping both the Atari emulator and NN on the GPU, they were able to get Pong solved within one to two minutes and reach FPS of 50k (on the advantage actor-critic (A2C) method, which will be the subject of the next part of the book).

Unfortunately, at the time of writing, the code wasn't stable enough. I failed to make it work on my hardware, but I hope that when you read this, the situation will have already changed. In any case, this project shows a somewhat extreme, but very efficient, way to increase RL methods' performance...

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Deep Reinforcement Learning Hands-On. - Second Edition
Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994
© 2020 Packt Publishing Limited All Rights Reserved

Author (1)

author image
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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