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

The A2C baseline

To establish the baseline results, we will use the A2C method in a very similar way to the code in the previous chapter.

Implementation

The complete source is in files Chapter19/01_train_a2c.py and Chapter19/lib/model.py. There are a few differences between this baseline and the version we used in the previous chapter. First of all, there are 16 parallel environments used to gather experience during the training. The second difference is the model structure and the way that we perform exploration. To illustrate them, let's look at the model and the agent classes.

Both the actor and critic are placed in the separate networks without sharing weights. They follow the approach used in the previous chapter, with our critic estimating the mean and the variance for the actions. However, now variance is not a separate head of the base network; it is just a single parameter of the model. This parameter will be adjusted during the training by SGD, but it doesn...

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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

The A2C baseline

Implementation

Unlock this book and the full library FREE for 7 days

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