Transfer of learning by composing solutions of elemental sequential tasks
type: Item
instance of: scholarly article
title: Transfer of learning by composing solutions of elemental sequential tasks
zbMATH Open document ID: 0772.68073
DOI: 10.1007/BF00992700
author: Satinder Pal Singh
published in: Machine Learning
publication date: 1992-08-11T00:00:00Z
review text: A class of sequential decision tasks (SDTs) is considered, called composite sequential decision tasks, formed by temporally concatenating a number of elemental sequential decision tasks. Elemental SDTs cannot be decomposed into simpler SDTs. A learning agent has to learn to solve a set of elemental and composite SDTs is examined. It is assumed that the structure of the composite tasks is unknown to the learning agent. The straightforward application of reinforcement learning to multiple tasks requires learning the tasks separately, which can waste computational resources, both memory and time. A new learning algorithm and a modular architecture is described that learns the decomposition of composite SDTs, and achieves transfer of learning by sharing the solutions of elemental SDTs across multiple composite SDTs. The solution of a composite SDT is constructed by computationally inexpensive modifications of the solutions of its constituent elemental SDTs. A proof of one aspect of the learning algorithm is provided.
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 945
zbMATH Keywords: reinforcement
zbMATH Keywords: compositional learning
zbMATH Keywords: transfer of learning
zbMATH Keywords: modular architecture
MaRDI profile type: Publication
cites work: Q3795523
cites work: Q4403756
cites work: Macro-operators: A weak method for learning
cites work: Q3683893
cites work: \({\mathcal Q}\)-learning
full work available at URL: https://doi.org/10.1007/bf00992700
OpenAlex ID: W2012036715
description: scientific article; zbMATH DE number 945