COEVOLUTION IN THE SUCCESSFUL LEARNING OF BACKGAMMON STRATEGY

Following Tesauro's work on TD-Gammon, we used a 4,000 parameter Feedf orward neural network to develop a competitive backgammon evaluation f unction. Play proceeds by a roll of the dice, application of the netwo rk to all legal moves, and selection of the position with the highest evaluation. However, no backpropagation, reinforcement or temporal dif ference learning methods were employed. Instead we apply simple hillcl imbing in a relative fitness environment. We start with an initial cha mpion of all zero weights and proceed simply by playing the current ch ampion network against a slightly mutated challenger and changing weig hts if the challenger wins. Surprisingly, this worked rather well. We investigate how the peculiar dynamics of this domain enabled a previou sly discarded weak method to succeed, by preventing suboptimal equilib ria in a ''meta-game'' of self-learning.