Learning to Drive in the Open Racing Car Simulator Using Online Neuroevolution

In this paper, we applied online neuroevolution to evolve nonplayer characters for The Open Racing Car Simulator (TORCS). While previous approaches allowed online learning with performance improvements during each generation, our approach enables a finer grained online learning with performance improvements within each lap. We tested our approach on three tracks using two methods of online neuroevolution (NEAT and rtNEAT) combined with four evaluation strategies ( -greedy, -greedy-improved, softmax, and interval-based) taken from the literature. We compared the eight resulting configurations on several driving tasks involving the learning of a driving behavior for a specific track, its adaptation to a new track, and the generalization capability to unknown tracks. The results we present show that, notwithstanding the several challenges that online learning poses, our approach 1) can successfully evolve drivers from scratch, 2) can also be used to transfer evolved knowledge to other tra...

• Type: Journal
• Year: 2010
• Where: TCIAIG
• Authors: Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi
• Keywords: Evaluation Strategies, Online, Online Neuroevolution, Software Engineering, TCIAIG 2010