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.
Similar content being viewed by others
Notes
Videos of this evolution are available online: http://www.irit.fr/~Sylvain.Cussat-Blanc/GRNDriver/index_en.php.
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.
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.
References
A. Agapitos, J. Togelius, S.M. Lucas, Evolving controllers for simulated car racing using object oriented genetic programming. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation. ACM (2007), pp. 1543–1550
C. Athanasiadis, D. Galanopoulos, A. Tefas, Progressive neural network training for the open racing car simulator. In IEEE Conference on Computational Intelligence and Games (CIG), 2012. IEEE (2012), pp. 116–123
W. Banzhaf, in Artificial regulatory networks and genetic programming, eds. by R.L. Riolo, B. Worzel. Genetic Programming Theory and Practice, chap 4 (2003), pp. 43–62
M. Bednár, A. Brček, B. Marek, M. Florek, V. Juhász’, J. Kosmel’, I. Valenčík, The modular architecture of an autonomous vehicle controller.
M.V. Butz, T.D. Lönneker, Optimized sensory-motor couplings plus strategy extensions for the torcs car racing challenge. In Proceedings of the 5th International Conference on Computational Intelligence and Games, CIG’09.IEEE Press, Piscataway, NJ, USA (2009), pp. 317–324
L. Cardamone, D. Loiacono, P.L. Lanzi. Evolving competitive car controllers for racing games with neuroevolution. In: Proceedings of the 11th Annual conference on Genetic and evolutionary computation, GECCO ’09 pp. 1179–1186. ACM, New York, NY, USA (2009)
L. Cardamone, D. Loiacono, P.L. Lanzi, On-line neuroevolution applied to the open racing car simulator. In Proceedings of the Eleventh conference on Congress on Evolutionary Computation, CEC’09. IEEE Press, Piscataway, NJ, USA (2009), pp. 2622–2629
L. Cardamone, D. Loiacono, P.L. Lanzi, Learning to drive in the open racing car simulator using online neuroevolution. IEEE Trans. Comput. Intell. AI in Games 2(3), 176–190 (2010)
S. Cussat-Blanc, N. Bredeche, H. Luga, Y. Duthen, M. Schoenauer, Artificial gene regulatory networks and spatial computation: a case study. In Proceedings of the European Conference on Artificial Life (ECAL’11). MIT Press, Cambridge, MA (2011)
S. Cussat-Blanc, J. Pollack, A cell-based developmental model to generate robot morphologies. In Proceedings of the 14th Annual Conference on Genetic and Evolutionary Computation. ACM New York, NY, USA (2012)
S. Cussat-Blanc, J. Pollack, Using pictures to visualize the complexity of gene regulatory networks. Artif. Life 13, 491–498 (2012)
S. Cussat-Blanc, S. Sanchez, Y. Duthen, Simultaneous cooperative and conflicting behaviors handled by a gene regulatory network. In IEEE Congress on Evolutionary Computation (CEC), 2012, pp. 1–8. IEEE (2012)
R. Doursat, Organically grown architectures: creating decentralized, autonomous systems by embryomorphic engineering. In Organic Computing, IX (Springer, 2008), pp. 167–200
P. Eggenberger Hotz, Combining developmental processes and their physics in an artificial evolutionary system to evolve shapes. In On Growth Form and Computers (Elsevier 2003), pp. 302–318
D.M. Fernández, A.J. .Fernández-Leiva, Una experiencia de diseño de controladores en juegos de carreras de coche mediante algoritmos evolutivos multiobjetivos y sistemas expertos. In VIII Congreso Español sobre Metaheurística, Algoritmos Evolutivos y Bioinspirados, ed. by J.A. Gámez et al. (UCLM, Albacete, 2012), pp. 683–690
H. Guo, Y. Meng, Y. Jin, A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems 98(3), 193–203 (2009)
K.I. Harrington, E. Awa. S. Cussat-Blanc, J. Pollack, Robot coverage control by Evolved Neuromodulation. In IJCNN 2013 (2013)
M. Joachimczak, B. Wróbel, Evolving gene regulatory networks for real time control of foraging behaviours. In Proceedings of the 12th International Conference on Artificial Life (2010)
M. Joachimczak, B. Wróbel, Evolution of the morphology and patterning of artificial embryos: scaling the tricolour problem to the third dimension. In Advances in Artificial Life. Darwin Meets von Neumann. ECAL 2009, Budapest, Hungary, Revised Selected Papers, Part II, ed. by G. Kampis, I. Karsai, E. Szathmary (Springer, 2011), pp. 35–43
J. Knabe, M. Schilstra, C. Nehaniv, Evolution and morphogenesis of differentiated multicellular organisms: autonomously generated diffusion gradients for positional information. Artif. Life XI 11, 321 (2008)
R. Lifton, M. Goldberg, R. Karp, D. Hogness, The organization of the histone genes in drosophila melanogaster: functional and evolutionary implications. In Cold Spring Harbor Symposia on Quantitative Biology, Cold Spring Harbor, NY, vol 42 (1978), pp. 1047–1051
D. Loiacono, L. Cardamone, P.L. Lanzi, Simulated car racing championship: competition software manual. CoRR (2013)
D. Loiacono, P.L. Lanzi, J. Togelius, E. Onieva, D.A. Pelta, M.V. Butz, T.D. Lönneker, L. Cardamone, D. Perez, Y. Sáez et al., The 2009 simulated car racing championship. IEEE Trans. Comput. Intell. AI in Games2(2), 131–147 (2010)
D. Loiacono, J. Togelius, P.L. Lanzi, L. Kinnaird-Heether, S.M. Lucas, M. Simmerson, D. Perez, R.G. Reynolds, Y. Saez. The wcci 2008 simulated car racing competition. In: IEEE Symposium on Computational Intelligence and Games, 2008. CIG’08. IEEE (2008), pp. 119–126
M. Nicolau, M. Schoenauer, W. Banzhaf, Evolving genes to balance a pole. In A.I. Esparcia-Alcazar, A. Ekart, S. Silva, S. Dignum, A.S. Uyar (eds.) Proceedings of the 13th European Conference on Genetic Programming, EuroGP 2010 ,vol 6021. LNCS, (2010), pp. 196–207
E. Onieva, D.A. Pelta, J. Alonso, V. Milanés, J. Pérez, A modular parametric architecture for the torcs racing engine. In Proceedings of the 5th International Conference on Computational Intelligence and Games, CIG’09. IEEE Press, Piscataway, NJ, USA (2009), pp. 256–262
E. Onieva, D.A. Pelta, J. Godoy, V. Milanés, J. Pérez, An evolutionary tuned driving system for virtual car racing games: the autopia driver. Int. J.Intelli. Syst. 27(3), 217–241 (2012)
M. Preuss. J. Quadflieg. G. Rudolph. Torcs sensor noise removal and multi-objective track selection for driving style adaptation. In: IEEE Conference on Computational Intelligence and Games (CIG), 2011. IEEE (2011), pp. 337–344
J. Quadflieg. M. Preuss, O. Kramer, G. Rudolph. Learning the track and planning ahead in a car racing controller. In: 2010 IEEE Symposium on Computational Intelligence and Games (CIG), IEEE (2010), pp. 395–402
J. Quadflieg, M. Preuss, G. Rudolph, Driving faster than a human player. In Proceedings of the 2011 International Conference on Applications of Evolutionary Computation-Volume Part I. Springer (2011), pp. 143–152
T. Reil, Dynamics of gene expression in an artificial genome-implications for biological and artificial ontogeny. Lecture notes in computer science (1999), pp. 457–466
K. Stanley, R. Sherony, N. Kohl, R. Miikkulainen, Neuroevolution of an automobile crash warning system. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) (2005)
K.O. Stanley, R. Miikkulainen. Evolving neural networks through augmenting topologies. Evol. Comput. 10, 99–127 (2002)
J. Togelius, S.M. Lucas, Evolving robust and specialized car racing skills. In IEEE Congress on Evolutionary Computation. CEC 2006. IEEE (2006), pp. 1187–1194
D. Wilson, E. Awa, S. Cussat-Blanc, K. Veeramachaneni, U.M. O’Reilly. On learning to generate wind farm layouts. In Proceeding of the Fifteenth Annual Conference on Genetic and Evolutionary Computation Conference. ACM (2013), pp. 767–774
L. Wolpert, Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol. 25(1), 1 (1969)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10710-014-9228-y