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Genetic Programming and Evolvable Machines

, Volume 15, Issue 4, pp 477–511 | Cite as

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

  • Stéphane SanchezEmail author
  • Sylvain Cussat-Blanc
Article

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.

Keywords

Gene regulatory network Virtual car racing Machine learning Incremental evolution 

References

  1. 1.
    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. 1543–1550Google Scholar
  2. 2.
    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. 116–123Google Scholar
  3. 3.
    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. 43–62Google Scholar
  4. 4.
    M. Bednár, A. Brček, B. Marek, M. Florek, V. Juhász’, J. Kosmel’, I. Valenčík, The modular architecture of an autonomous vehicle controller.Google Scholar
  5. 5.
    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. 317–324Google Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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. 2622–2629Google Scholar
  8. 8.
    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)CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    S. Cussat-Blanc, J. Pollack, Using pictures to visualize the complexity of gene regulatory networks. Artif. Life 13, 491–498 (2012)Google Scholar
  12. 12.
    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)Google Scholar
  13. 13.
    R. Doursat, Organically grown architectures: creating decentralized, autonomous systems by embryomorphic engineering. In Organic Computing, IX (Springer, 2008), pp. 167–200Google Scholar
  14. 14.
    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. 302–318Google Scholar
  15. 15.
    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. 683–690Google Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    K.I. Harrington, E. Awa. S. Cussat-Blanc, J. Pollack, Robot coverage control by Evolved Neuromodulation. In IJCNN 2013 (2013)Google Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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. 35–43Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    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. 1047–1051Google Scholar
  22. 22.
    D. Loiacono, L. Cardamone, P.L. Lanzi, Simulated car racing championship: competition software manual. CoRR (2013)Google Scholar
  23. 23.
    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)CrossRefGoogle Scholar
  24. 24.
    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. 119–126Google Scholar
  25. 25.
    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. 196–207Google Scholar
  26. 26.
    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. 256–262Google Scholar
  27. 27.
    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)CrossRefGoogle Scholar
  28. 28.
    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. 337–344Google Scholar
  29. 29.
    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. 395–402Google Scholar
  30. 30.
    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. 143–152Google Scholar
  31. 31.
    T. Reil, Dynamics of gene expression in an artificial genome-implications for biological and artificial ontogeny. Lecture notes in computer science (1999), pp. 457–466Google Scholar
  32. 32.
    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)Google Scholar
  33. 33.
    K.O. Stanley, R. Miikkulainen. Evolving neural networks through augmenting topologies. Evol. Comput. 10, 99–127 (2002)Google Scholar
  34. 34.
    J. Togelius, S.M. Lucas, Evolving robust and specialized car racing skills. In IEEE Congress on Evolutionary Computation. CEC 2006. IEEE (2006), pp. 1187–1194Google Scholar
  35. 35.
    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. 767–774Google Scholar
  36. 36.
    L. Wolpert, Positional information and the spatial pattern of cellular differentiation. J. Theor. Biol. 25(1), 1 (1969)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.IRIT - CNRS UMR 5505University of ToulouseToulouse France

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