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Fahimeh, G., Mehridehnavi, A., Pérez-Garrido, A. and Pérez-Sánchez, H. (2018) Neural Network and Deep-Learning Algorithms Used in QSAR Studies: Merits and Drawbacks. Drug Discovery Today, 23, 1784-1790.
https://doi.org/10.1016/j.drudis.2018.06.016

has been cited by the following article:

• TITLE: Analysis of Soft Decision Trees for Passive-Expert Reinforcement Learning

  AUTHORS: Jonathan Martini, Daniel J. Fonseca

  KEYWORDS: Deep Learning, Soft Decision Trees, Passive Reinforcement Learning, Recurrent Neural Networks

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.12 No.2, May 31, 2022

  ABSTRACT: This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN). The soft decision tree networks were built using the PyTorch machine learning and the OpenAi’s Gym environment frameworks. The conducted research study aimed at assessing the performance of soft decision tree networks on Cartpole as provided in the OpenAi Gym software package. The baseline performance metric that the soft decision tree networks were compared against was a simple Deep Neural Network using several linear layers with ReLU and Softmax activation functions for the input and output layers, respectively. All networks were trained using the Backpropagation algorithm provided generically by PyTorch’sAutograd module.