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 training process

Now that you know how the state of the cube is encoded in a 20 × 24 tensor, let's talk about the NN architecture and how it is trained.

The NN architecture

On the figure that follows (taken from the paper), the network architecture is shown.

Figure 24.2: The NN architecture transforming the observation (top) to the action and value (bottom)

As the input, it accepts the already familiar cube state representation as a 20 × 24 tensor and produces two outputs:

The policy, which is a vector of 12 numbers, representing the probability distribution over our actions.
The value, a single scalar estimating the "goodness" of the state passed. The concrete meaning of a value will be discussed later.

Between the input and output, the network has several fully connected layers with exponential linear unit (ELU) activations. In my implementation, the architecture is exactly the same as in the paper, and the model is...

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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 training process

The NN architecture

Unlock this book and the full library FREE for 7 days

Author (1)

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