Controller for TORCS created by imitation

Abstract

This paper is an initial approach to create a controller for the game TORCS by learning how another controller or humans play the game. We used data obtained from two controllers and from one human player. The first controller is the winner of the WCCI 2008 Simulated Car Racing Competition, and the second one is a hand coded controller that performs a complete lap in all tracks. First, each kind of controller is imitated separately, then a mix of the data is used to create new controllers. The imitation is performed by means of training a feed forward neural network with the data, using the backpropagation algorithm for learning.