Computationally intensive and noisy tasks: co-evolutionary learning and temporal difference learning on Backgammon

摘要

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 Tasauro used for temporal difference learning to create the Backgammon strategy "Pubeval", co-evolutionary learning here creates a better player.