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

Correlation and sample efficiency

One of the approaches to improving the stability of the policy gradient family of methods is using multiple environments in parallel. The reason behind this is the fundamental problem we discussed in Chapter 6, Deep Q-Networks, when we talked about the correlation between samples, which breaks the independent and identically distributed (i.i.d.) assumption, which is critical for stochastic gradient descent (SGD) optimization. The negative consequence of such correlation is very high variance in gradients, which means that our training batch contains very similar examples, all of them pushing our network in the same direction.

However, this may be totally the wrong direction in the global sense, as all those examples may be from one single lucky or unlucky episode.

With our deep Q-network (DQN), we solved the issue by storing a large number of previous states in the replay buffer and sampling our training batch from this buffer. If the buffer...

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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

Correlation and sample efficiency

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Author (1)

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