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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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A3C with data parallelism

The first version of A3C parallelization that we will check (which was outlined in Figure 13.2) has both one main process that carries out training and several child processes communicating with environments and gathering experience to train on.

Implementation

For simplicity and efficiency, the NN weights broadcasting from the trainer process are not implemented. Instead of explicitly gathering and sending weights to child processes, the network is shared between all processes using PyTorch built-in capabilities, allowing us to use the same nn.Module instance with all its weights in different processes by calling the share_memory() method on NN creation. Under the hood, this method has zero overhead for CUDA (as GPU memory is shared among all the host's processes), or shared memory inter-process communication (IPC) in the case of CPU computation. In both cases, the method improves performance, but limits our example of one single machine using one...

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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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