Car Setup Optimization Competition @ EvoStar 2010

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The contest involved three tracks. The optimization algorithm had to find the best car setup for each one of the tracks. The contest was divided into an optimization phase and an evaluation phase.
During the optimization phase, the optimization algorithm was applied to search for the best parameter setting. During the evaluation phase, the best solution was scored according to the distance covered in a fixed amount of game time.

A parameter setting is represented by a vector of real numbers. The competition software provides an API to evaluate a specific parameter setting on a track and returns the best lap time, the top speed, the distance raced, and the damage suffered. Through the API, it is possible to specify the amount of game ticks to use for evaluating a car setup. The game tics spent for an evaluation are subtracted from the total amount of game tics available. When the 1 millions of game ticks are exhausted or the evaluation process has taken up more than 2 hours of CPU time, no further evaluation will be possible.

Organizers
•Luigi Cardamone, Politecnico di Milano, Italy
•Daniele Loiacono, Politecnico di Milano, Italy
•Markus Kemmerling, TU Dortmund, Germany
•Mike Preuss, TU Dortmund, Germany

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Car Setup Optimization Competition @ EvoStar 2010

  1. 1. & TU Dortmund Car Setup Optimization Competition @ EVOStar 2010 Luigi Cardamone, Daniele Loiacono, Markus Kemmerling, Mike Preuss Car Setup Optimization @ EVOStar-2010
  2. 2. What is the competition about?  The goal is to submit an optimization algorithm for a challenging problem  The problem: find the best car setup that achieves the highest performance on a previously unknown track  The submitted algorithms will be compared on three different tracks TU Dortmund Car Setup Optimization @ EVOStar-2010
  3. 3. Optimizing Car Setup in TORCS
  4. 4. Optimizing Car Setup As in real racing competition, finding a good car racing setup is a very challenging problem.  The best set of parameters depends on: The track The driver’s style (in this case AI controller) The final measure  Challenges: Limited amount of time available for the optimization Evaluation times must be determined by the algorithm Noisy fitness function TU Dortmund Car Setup Optimization @ EVOStar-2010
  5. 5. Which parameters?  A car presents many parameters that can be optimized: Gear ratios Rear/Front wing angle Brakes Rear differential Rear/Front anti-roll bars Wheels • Ride • Toe • Camber Suspensions • Spring • Bell crank … TU Dortmund Car Setup Optimization @ EVOStar-2010
  6. 6. Optimization Framework  The competition framework creates a physical separation between the optimizer and the TORCS engine: Freedom to choose the programming language of the optimizer Restrict the access only to a set of parameters defined by the designer Parameter Optimization sequence TORCS Algorithm Client Server UDP Fitness evaluation TU Dortmund Car Setup Optimization @ EVOStar-2010
  7. 7. The optimization process  The parameters involved in the optimization are normalized in the range [0,1]  The optimizer iteratively asks to the TORCS server to evaluate a given sequence of parameters  The optimizer must also specify the duration of the each evaluation (specified in game tics)  Each time an evaluation request arrives, the server: 1. Plug in the parameters in the car setup 2. A programmed policy (Berniw2) drives the car for the specified amount of time 3. Finally, the evaluation results are returned as: lap time, distance raced, top speed and damages TU Dortmund Car Setup Optimization @ EVOStar-2010
  8. 8. Submissions
  9. 9. The competitors  Five new entries have been submitted to the competition: Jorge Muñoz Fuentes - Universidad Carlos III de Madrid, Spain - MOEA Moisés Martínez Muñoz, Emilio Martín Gallardo, Yago Saez Achaerandio – Universidad Carlos III de Madrid, Spain – (12+10)-EA Pablo José García Evans, Yago Saez Achaerandio – Universidad Carlos III de Madrid, Spain – PSO Wolfgang Walz, TU Dortmund, Germany - PSO Antonio J. Fernández, Carlos Cotta, Alberto Fuentes – ?  Entries (not ranked) of the organizers: Basic Genetic Algorithm (Luigi Cardamone et al., Politecnico di Milano) CMA-ES with well chosen parameters (Markus Kemmerling, TU Dortmund) TU Dortmund Car Setup Optimization @ EVOStar-2010
  10. 10. The Results
  11. 11. Scoring process  Scoring process involved three tracks (unknown to the competitors): Dirt 3 (a dirty track) Poli-Track (unknown) CG Track 2 (rather easy and fast)  For each track, 10 runs of each optimization technique have been performed  Each run is stopped after that 1.000.000 of game tics expires (approximately 2 minutes of computation)  The champion of each run was evaluated scoring the distance raced in the first 10.000 game tics of a race TU Dortmund Car Setup Optimization @ EVOStar-2010
  12. 12. 2010 Tracks Pictures Dirt-3 Poli-track CG track 2 TU Dortmund Car Setup Optimization @ EVOStar-2010
  13. 13. CG track 2 12000 10000 8000 6000 4000 2000 avg 0 var TU Dortmund Car Setup Optimization @ EVOStar-2010
  14. 14. Poli-track 9000 8000 7000 6000 5000 4000 3000 2000 1000 avg 0 var TU Dortmund Car Setup Optimization @ EVOStar-2010
  15. 15. Dirt-3 7000 6000 5000 4000 3000 2000 1000 avg 0 var TU Dortmund Car Setup Optimization @ EVOStar-2010
  16. 16. And now some Points (as in Formula-1): Competitor CG track 2 Poli-track Dirt-3 Overall Munoz-MOEA 10 Garcia/Saez-PSO 6 Walz-PSO 8 Fernandez/Cotta/Fuentes 4 Munoz/Martin/Saez-EA 5 TU Dortmund Car Setup Optimization @ EVOStar-2010
  17. 17. And now some Points (as in Formula-1): Competitor CG track 2 Poli-track Dirt-3 Overall Munoz-MOEA 10 6 Garcia/Saez-PSO 6 10 Walz-PSO 8 5 Fernandez/Cotta/Fuentes 4 4 Munoz/Martin/Saez-EA 5 8 TU Dortmund Car Setup Optimization @ EVOStar-2010
  18. 18. And now some Points (as in Formula-1): Competitor CG track 2 Poli-track Dirt-3 Overall Munoz-MOEA 10 6 8 Garcia/Saez-PSO 6 10 5 Walz-PSO 8 5 6 Fernandez/Cotta/Fuentes 4 4 10 Munoz/Martin/Saez-EA 5 8 4 TU Dortmund Car Setup Optimization @ EVOStar-2010
  19. 19. And now some Points (as in Formula-1): Competitor CG track 2 Poli-track Dirt-3 Overall Munoz-MOEA 10 6 8 24 Garcia/Saez-PSO 6 10 5 21 Walz-PSO 8 5 6 19 Fernandez/Cotta/Fuentes 4 4 10 18 Munoz/Martin/Saez-EA 5 8 4 17 And the winner is: Jorge Muñoz Fuentes - MOEA Universidad Carlos III de Madrid, Spain TU Dortmund Car Setup Optimization @ EVOStar-2010
  20. 20. ……and what about the organizers? Competitor CG track 2 Poli-track Dirt-3 Overall Munoz-MOEA 9831.83 7654.01 6128.29 23614.13 Garcia/Saez-PSO 8386.77 7979.86 5021.41 21388.04 Walz-PSO 8408.35 7304.54 5336.88 21049.77 Fernandez/Cotta/Fuentes 7553.21 5931.47 6263.40 19748.08 Munoz/Martin/Saez-EA 8167.60 7718.36 4629.33 20515.29 Cardamone-SimpleGA 9563.08 7273.06 5932.09 22768.23 Kemmerling-CMA_ES 10410.13 8392.49 6415.87 25218.49 TU Dortmund Car Setup Optimization @ EVOStar-2010
  21. 21. Conclusions  Good news: We had 5 new entries! Wow! The center of car optimization in Europe is in Spain :-)  And: Every track was won by a different algorithm The winner algorithm is multi-objective and uses all 4 available return values: top speed, distanced raced, damage and lap time 3rd to 5th place very tight…  But: It is obviously not that easy to beat the SimpleGA Organizer entries show that there is still some potential TU Dortmund Car Setup Optimization @ EVOStar-2010

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