Computationally intensive and noisy tasks: Co-evolutionary learning and temporal difference learning

Abstract

The most difficult but realistic learning tasks are both noisy and computationally intensive. This paper investigates how, for a given solution representation, co-evolutionary learning can achieve the highest ability from the least computation time. Using a population of Backgammon strategies, this paper examines ways to make computational costs reasonable. With the same simple architecture Gerald Tesauro used for Temporal Difference learning to create the Backgammon strategy `Pubeval', co-evolutionary learning here creates a better player