Generating Artificial Neural Networks for Value Function Approximation in a Domain Requiring a Shifting Strategy

Abstract

Artificial neural networks have been successfully used as approximating value functions for tasks involving decision making. In domains where a shift in judgment for decisions is necessary as the overall state changes, it is hypothesized that multiple neural networks are likely be beneficial as an approximation of a value function over those that employ a single network. For our experiments, the card game Dominion was chosen as the domain. This work compares neural networks generated by various machine learning methods successfully applied to other games (such as in TD-Gammon) to a genetic algorithm method for generating two neural networks for different phases of the game along with evolving a transition point. The results demonstrate a greater success ratio with the method hypothesized. This suggests future work examining more complex multiple neural network configurations could apply to this game domain as well as being applicable to other problems.