Learning Evaluation Function for Decision Making of Soccer Agents Using Learning to Rank.

In the simulated soccer domain, the evaluation of actions that can be performed by soccer agents is important to design the team behavior. It is not easy to design the evaluation function that reflects the intention of human developers. Usually, manual adjustment of the evaluation function requires much cost. We propose a method to apply Learning to Rank algorithm to the evaluation function of the decision making of soccer agents. The experimental results show Learning to Rank would be promising to using the instruction of human trainer.