Performance Enhancement of Deep Reinforcement Learning Networks Using Feature Extraction

@inproceedings{Ollero2018PerformanceEO,
  title={Performance Enhancement of Deep Reinforcement Learning Networks Using Feature Extraction},
  author={Joaquin Ollero and Christopher Child},
  booktitle={ISNN},
  year={2018}
}
The combination of Deep Learning and Reinforcement Learning, termed Deep Reinforcement Learning Networks (DRLN), offers the possibility of using a Deep Learning Neural Network to produce an approximate Reinforcement Learning value table that allows extraction of features from neurons in the hidden layers of the network. This paper presents a two stage technique for training a DRLN on features extracted from a DRLN trained on a identical problem, via the implementation of the Q-Learning… 

DEEP REINFORCEMENT LEARNING WITH SPARSE DISTRIBUTED MEMORY FOR “WATER WORLD” PROBLEM SOLVING

  • Udc
  • Computer Science
  • 2021
TLDR
Improved reinforcement learning parameters on the example of the “Water World” problem were improved by increasing the accuracy of the model of the physical process represented by a deep neural network.

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