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In this paper, we apply imitation learning to develop drivers for The Open Racing Car Simulator (TORCS). Our approach can be classified as a direct method in that it applies supervised learning to learn car racing behaviors from the data collected from other drivers. In the literature, this approach is known to have led to extremely poor performance with drivers capable of completing only very small parts of a track. In this paper we show that, by using high-level information about the trackdoi:10.1109/cig.2009.5286480 dblp:conf/cig/CardamoneLL09 fatcat:jewvxvdxzrf5plvtsrrujzevjm