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.