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 simple clicking approach

As the first demo, let's implement a simple A3C agent that decides where it should click given the image observation. This approach can solve only a small subset of the full MiniWoB suite, and we will discuss restrictions of this approach later. For now, it will allow us to get a better understanding of the problem.

As with the previous chapter, due to its size, I won't put the complete source code here. We will focus on the most important functions and I will provide the rest as an overview. The complete source code is available in the GitHub repository.

Grid actions

When we talked about Universe's architecture and organization, it was mentioned that the richness and flexibility of the action space creates a lot of challenges for the RL agent. MiniWoB's active area inside the browser is just 160×210 (exactly the same dimension that the Atari emulator has), but even with such a small area, our agent could be asked to move...

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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 simple clicking approach

Grid actions

Unlock this book and the full library FREE for 7 days

Author (1)

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