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

Further improvements and experiments

There are lots of directions and things that could be tried:

More input and network engineering: the cube is a complicated thing, so simple feed-forward NNs may not be the best model. Probably, the network could greatly benefit from convolutions.
Oscillations and instability during training might be a sign of a common RL issue with inter-step correlations. The usual approach is the target network, when we use the old version of the network to get bootstrapped values.
The priority replay buffer might help the training speed.
My experiments show that the samples' weighting (inversely proportional to the scramble depth) helps to get a better policy that knows how to solve slightly scrambled cubes, but might slow down the learning of deeper states. Probably, this weighting could be made adaptive to make it less aggressive in later training stages.
Entropy loss could be added to the training to regularize our policy.

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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

Further improvements and experiments

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Author (1)

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