A Threshold-based Scheme for Reinforcement Learning in Neural Networks
Machine Learning
2017-01-17 v4 Neural and Evolutionary Computing
Abstract
A generic and scalable Reinforcement Learning scheme for Artificial Neural Networks is presented, providing a general purpose learning machine. By reference to a node threshold three features are described 1) A mechanism for Primary Reinforcement, capable of solving linearly inseparable problems 2) The learning scheme is extended to include a mechanism for Conditioned Reinforcement, capable of forming long term strategy 3) The learning scheme is modified to use a threshold-based deep learning algorithm, providing a robust and biologically inspired alternative to backpropagation. The model may be used for supervised as well as unsupervised training regimes.
Cite
@article{arxiv.1609.03348,
title = {A Threshold-based Scheme for Reinforcement Learning in Neural Networks},
author = {Thomas H. Ward},
journal= {arXiv preprint arXiv:1609.03348},
year = {2017}
}