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

Training both tigers and deer

The next example is the scenario when both tigers and deer are controlled by different DQN models being trained simultaneously. Tigers are rewarded for living longer, which means eating more deer, as at every step in the simulation they lose health points. Deer are also rewarded on every timestamp.

The code is in Chapter25/forest_both_dqn.py and it is quite a simple extension of the previous example. For both groups of agents, we have a separate Agent class instance, which communicates with the environment. As the observation for both groups is different, we have two separate networks, replay buffers, and experience sources. On every training step, we sample batches from both replay buffers and then train both networks independently.

I'm not going to put the code here, as it differs from the previous example only in small details. If you are curious, you can check GitHub examples. The following are plots with the convergence results.

...

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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.
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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Training both tigers and deer

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