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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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Tuning hyperparameters

In the previous section, we had Pong solved in three hours of optimization and 9 million frames. Now is a good time to tweak our hyperparameters to speed up convergence. The golden rule here is to tweak one option at a time and make conclusions carefully, as the whole process is stochastic.

In this section, we will start with the original hyperparameters and perform the following experiments:

  • Increase the learning rate
  • Increase the entropy beta
  • Change the count of environments that we are using to gather experience
  • Tweak the size of the batch

Strictly speaking, the following experiments weren't proper hyperparameter tuning but just an attempt to get a better understanding of how A2C convergence dynamics depend on the parameters. To find the best set of parameters, the full grid search or random sampling of values could give much better results, but they would require much more time and resources.

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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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