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 battle between equal actors

The final example in this chapter is the situation when one policy drives fighting between two groups of identical agents. This version is implemented in Chapter25/battle_dqn.py. The code is straightforward and won't be put here.

I did only a couple of experiments with the code, so hyperparameters could be improved. In addition, you can experiment with the training process. In the code, both groups are driven by the same policy that we are optimizing, which may not be the best approach. Instead, you can experiment with an AlphaGo Zero style of training, when the best policy is used for one group and another group is driven by the policy that we are optimizing at the moment. Once the best policy starts to consistently lose, it is updated. In this case, the optimized policy may have time to learn all the tricks and weaknesses of the current best policy, which may start an improvement loop.

In my experiments, the training wasn't very stable...

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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

The battle between equal actors

Unlock this book and the full library FREE for 7 days

Author (1)

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