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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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I2A on Atari Breakout

The training path of I2A is a bit complicated and includes a lot of code and several steps. To understand it better, let's start with a brief overview. In this example, we will implement the I2A architecture described in the paper [2], adopted to the Atari environments, and test it on the Breakout game. The overall goal is to check the training dynamics and the effect of imagination augmentation on the final policy.

Our example consists of three parts, which correspond to different steps in the training:

  1. The baseline advantage actor-critic (A2C) agent in Chapter22/01_a2c.py. The resulting policy is used for obtaining observations of the EM.
  2. The EM training in Chapter22/02_imag.py. It uses the model obtained on the previous step to train the EM in an unsupervised way. The result is the EM weights.
  3. The final I2A agent training in Chapter22/03_i2a.py. In this step, we use the EM from step 2 to train a full I2A agent, which combines the model...
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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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