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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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Results

Let's now take a look at the results.

The feed-forward model

The convergence on Yandex data for one year requires about 10M training steps, which can take a while. (GTX 1080 Ti trains at a speed of 230-250 steps per second.)

During the training, we have several charts in TensorBoard showing us what's going on.

Figure 10.3: The reward for episodes during the training

Figure 10.4: The reward for test episodes

The two preceding charts show the reward for episodes played during the training and the reward obtained from testing (which is done on the same quotes, but with epsilon=0). From them, we see that our agent is learning how to increase the profit from its actions over time.

Figure 10.5: The lengths of played episodes

Figure 10.6: The values predicted by the network on a subset of states

The lengths of episodes also increased after 1M training iterations. The number of values predicted by the network is growing.

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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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