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Present car racing_setup Present car racing_setup Presentation Transcript

  • Car setup optimization via evolutionary algorithms Carlos Cotta, Antonio J. Fern´andez-Leiva, Alberto Fuentes S´anchez, Ra´ul Lara-Cabrera Dept. Lenguajes y Ciencias de la Computaci´on, University of M´alaga, SPAIN http://anyself.wordpress.com http://dnemesis.lcc.uma.es
  • Introduction Artificial intelligence (AI) in games has become a very important research field International conferences and journals that only focus on this topic: CIG, AIIDE, TCIAIG Games offer a large variety of AI research problems: planning, player modeling, decision making under uncertainty, ... They should be used as tool for testing AI techniques 2 / 11
  • TORCS: The Open Racing Car Simulator Open-source 3D racing simulator Human and artificial players (bots) Client-server architecture: Bots run as an external process Communication with the race server through an UDP connection Cars have 50 mechanical parameters: Tyre angles, suspension’s hardness, ... Good testing framework for optimization techniques 3 / 11
  • The competition The contest involves three tracks The objective is to find the best car setup for each one of the tracks Two phases: optimization and evaluation (time-limited) A car setup is represented by a vector of real numbers (50 parameters) Participants are ranked according to their maximum covered distance 4 / 11
  • Steady-state approach (I) Parameters are real values and encoded with 10-bit Each individual of the population is an array of 500 bits Crossover and mutation with probability 1.0 5 / 11
  • Steady-state approach (II) Fitness function C1 ∗ distraced + C2 ∗ topspeed + C3 ∗ (1000 − bestlap) + C4 ∗ damage distraced Total amount of distance topspeed Maximum speed bestlap Best lap time damage Damage taken by the car Several combinations of weights C1, C2, C3, C4 have been tested. 6 / 11
  • Steady-state approach (III) Experimental Analysis Runs:10 Population:50 Iterations:20 Best weights after testing several combinations: C1 = 0.6, C2 = 2.5, C3 = 0.15 and C4 = 0.05 Controller submitted to the EVO-* competition: Competitor CG Track Poli-Track Dirt-3 Distance Points Mu˜noz (MOEA) 10 6 8 23614.13 24 Garc´ıa-S´aez (PSO) 6 10 5 21388.04 21 Walz (PSO) 8 5 6 21049.77 19 Fuent-Cotta-Fdez-Cab (GA) 4 4 10 19748.08 18 Mu˜noz-Mart´ın-S´aez (EA) 5 8 4 20515.29 17 7 / 11
  • Multi-objective approach Multi-objective algorithm using SPEA2 We have tested several combinations of fitness functions: Variables: bestlap, distraced, damage, topspeed and the fitness defined for the single-objective algorithm Best results obtained from two objectives: minimize the time of the best lap and maximize the single-objective fitness Additionally, we have considered the optimization of every variable, that is, maximize distraced and topspeed and minimize bestlap and damage 8 / 11
  • Multi-objective approach (II) Experimental Analysis Runs:10 Population:50 Generations:20 Compared to the participants of the competition held at GECCO-2009 Driver Speedway ETRACK Olethros Wheel Total Multi-objective 10 5 8 8 31 V&M&C 4 8 5 10 27 Jorge 8 4 10 4 26 Multi-objective PCA 3 10 6 6 25 Single-objective 5 6 4 5 20 Luigi 6 3 3 3 15 9 / 11
  • Conclusions Different proposals based on evolutionary computation to set up a car in a racing simulator Multi-objective evolutionary algorithms are a good solution to the problem The single-objective algorithm has determined the fitness function used in our EMOAs Future work: Use meta-optimization to get a better fitness function Improve evolutionary algorithms’ parameters in order to obtain better results 10 / 11
  • Thanks for your attention! AnySelf Project http://facebook.com/AnySelfProject @anyselfproject http://dnemesis.lcc.uma.es/wordpress/ @DNEMESISproject 11 / 11