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Gene regulated car driving: using a gene regulatory network to drive a virtual car

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Abstract

This paper presents a virtual racing car controller based on an artificial gene regulatory network. Usually used to control virtual cells in developmental models, recent works showed that gene regulatory networks are also capable to control various kinds of agents such as foraging agents, pole cart, swarm robots, etc. This paper details how a gene regulatory network is evolved to drive on any track through a three-stages incremental evolution. To do so, the inputs and outputs of the network are directly mapped to the car sensors and actuators. To make this controller a competitive racer, we have distorted its inputs online to make it drive faster and to avoid opponents. Another interesting property emerges from this approach: the regulatory network is naturally resistant to noise. To evaluate this approach, we participated in the 2013 simulated racing car competition against eight other evolutionary and scripted approaches. After its first participation, this approach finished in third place in the competition.

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Notes

  1. http://torcs.sourceforge.net/.

  2. Videos of this evolution are available online: http://www.irit.fr/~Sylvain.Cussat-Blanc/GRNDriver/index_en.php.

  3. A video of the capacity of the GRN to handle the noise is available on-line: http://www.irit.fr/~Sylvain.Cussat-Blanc/GRNDriver/index_en.php.

  4. The warm-up stage consists of 100,000 timesteps that can be used by the competitors in order to collect data about an unknown track.

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Correspondence to Stéphane Sanchez.

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Sanchez, S., Cussat-Blanc, S. Gene regulated car driving: using a gene regulatory network to drive a virtual car. Genet Program Evolvable Mach 15, 477–511 (2014). https://doi.org/10.1007/s10710-014-9228-y

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