Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolving a real-world vehicle warning system
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Bayesian Imitation of Human Behavior in Interactive Computer Games
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Evolving competitive car controllers for racing games with neuroevolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Robust player imitation using multiobjective evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
On-line neuroevolution applied to the open racing car simulator
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Evolution of reactive rules in multi player computer games based on imitation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Human-assisted neuroevolution through shaping, advice and examples
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Learning, evolution and adaptation in racing games
Proceedings of the 9th conference on Computing Frontiers
Advanced overtaking behaviors for blocking opponents in racing games using a fuzzy architecture
Expert Systems with Applications: An International Journal
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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 track ahead of the car and by predicting high-level actions, it is possible to develop drivers with performances that in some cases are only 15% lower than the performance of the fastest driver available in TORCS. Our experimental results suggest that our approach can be effective in developing drivers with good performance in non-trivial tracks using a very limited amount of data and computational resources. We analyze the driving behavior of the controllers developed using our approach and identify perceptual aliasing as one of the factors which can limit performance of our approach.