A human-like TORCS controller for the Simulated Car Racing Championship

Proceeding of: IEEE Congres on Computational Intelligence and Games (CIG'10), Copenhagen (Denmark), 18-21, August, 2010.This paper presents a controller for the 2010 Simulated Car Racing Championship. The idea is not to create the fastest controller but a human-like controller. In order to achieve this, first we have created a process to build a model of the tracks while the car is running and then we used several neural networks which predict the trajectory the car should follow and the target speed. A scripted policy is used for the gear change and to follow the predicted trajectory with the predicted speed. The neural networks are trained with data retrieved from a human player, and are evaluated in a new track. The results shows an acceptable performance of the controller in unknown tracks, more than 20% slower than the human in the same tracks because of the mistakes made when the controller tries to follow the trajectory.This work was supported in part by the University Carlos III of Madrid under grant PIF UC3M01-0809 and by the Ministry of Science and Innovation under project TRA2007- 67374-C02-02