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 experiment results

Unfortunately, the paper provided no details about very important aspects of the method, like training hyperparameters, how deeply cubes were scrambled during the training, and the obtained convergence. To fill in the missing blanks, I did lots of experiments with various values of hyperparameters, but still my results are very different from those published in the paper. First of all, the training convergence of the original method is very unstable. Even with a small learning rate and a large batch size, the training eventually diverges, with the value loss component growing exponentially. Examples of this behavior are shown on the figure that follows.

Figure 24.5: The policy loss (left) and value loss (right) of two runs of the paper's method

After several experiments with this, I came to the conclusion that this behavior is a result of the wrong value objective being proposed in the method. Indeed, in the formula , the value returned by the...

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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 experiment results

Unlock this book and the full library FREE for 7 days

Author (1)

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