language: en

[As appears in Neural Information Processing Systems 4, pp. 251-258, 1992.]

The Efficient Learning of Multiple Task Sequences

Satinder P. Singh
Department of Computer Science University of Massachusetts
Amherst, MA 01003

Abstract I present a modular network architecture and a learning algorithm based on incremental dynamic programming that allows a single learning agent to learn to solve multiple Markovian decision tasks (MDTs) with significant transfer of learning across the tasks. I consider a class of MDTs, called composite tasks, formed by temporally concatenating a number of simpler, elemental MDTs. The architecture is trained on a set of composite and elemental MDTs. The temporal structure of a composite task is assumed to be unknown and the architecture learns to produce a temporal decomposition. It is shown that under certain conditions the solution of a composite MDT can be constructed by computationally inexpensive modifications of the solutions of its constituent elemental MDTs.

1 INTRODUCTION