2013 Simulated Car Racing @ GECCO-2013

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Simulated Car Racing Competition held during GECCO-2013

More information at
http://groups.google.com/group/racingcompetition
http://scr.geccocompetitions.com

Organizers
Daniele Loiacono, Politecnico di Milano
Pier Luca Lanzi, Politecnico di Milano

Published in: Technology, Sports, Automotive
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2013 Simulated Car Racing @ GECCO-2013

  1. 1. GECCO 2013 Simulated Car Racing Competition GECCO 2013 Simulated Car Racing Competition Daniele Loiacono and Pier Luca Lanzi
  2. 2. GECCO 2013 Simulated Car Racing Competition SCR in a nutshell q  Develop a driver for TORCS " hand-coded, " learned, " evolved, " … q  Three races on three unknown tracks q  Each race has the following structure: " Warm-up: each driver can explore the track and learn something useful " Qualifiers: each driver races alone against the clock (the best 8 drivers move to the race) " Actual race: all the drivers race together q  Drivers are scored based on their final position in the races, best lap-time, receiving the least amount of damages.
  3. 3. Competition Framework
  4. 4. GECCO 2013 Simulated Car Racing Competition The Open Racing Car Simulator & the Competition Software TORCS BOT BOT BOT TORCS PATCH SBOT SBOT SBOT BOT BOTBOT UDP UDPUDP q  The competition server q  Separates the bots from TORCS q  Build a well-defined sensor model q  Works in real-time
  5. 5. GECCO 2013 Simulated Car Racing Competition Sensors and actuators q  Rangefinders for edges on the track and opponents (with noise) q  Speed, RPM, fuel, damage, angle with track, distance race, position on track, etc. q  Six effectors: steering wheel [-1,+1], gas pedal [0, +1], brake pedal [0,+1], gearbox {-1,0,1,2,3,4,5,6}, clutch [0,+1], focus direction
  6. 6. Competitors
  7. 7. GECCO 2013 Simulated Car Racing Competition SCR 2013 Entrants q  State of the art: AUTOPIA, Madrid and Granada, Spain q  Entries " EVOR, University of Adelaide, Australia " Ahoora, University of Adelaide, Australia " GAZZELLE, Indiana University South Bend, USA " GRN Driver, University of Toulouse, France " ICER-IDDFS, Ritsumeikan University, Japan " Mr.Racer, TU Dortmund, Germany " Presto AI, Uwe Kadritzke, Germany " SnakeOil, Chris X Edwards, Switzerland
  8. 8. GECCO 2013 Simulated Car Racing Competition Industrial Computer Science Department. Centro de Automática y Robótica Consejo Superior de Investigaciones Científicas Madrid, Spain Contact:E. Onieva (enrique.onieva@car.upm-csic.es) AUTOPIA
  9. 9. GECCO 2013 Simulated Car Racing Competition AUTOPIA q  Fuzzy Architecture based on three basic modules for gear, steering and speed control " optimized with a genetic algorithm q  Learning in the warm-up stage: " Maintain a vector with as many real values as tracklength in meters. " Vector initialized to 1.0 " If the vehicle goes out of the track or suffers damage then multiply vector positions from 250 meters before the current position by 0.95. q  During the race the vector is multiplied by F to make the driver more cautious in function of the damage: " F=1-0.02*round(damage/1000)
  10. 10. GECCO 2013 Simulated Car Racing Competition EVOR (Evolutionary Racer) Samadhi Nallaperuma, Frank Naumann (supervisor) University of Adelaide, Austrailia
  11. 11. GECCO 2013 Simulated Car Racing Competition EVOR (Evolutionary Racer) q  Build a track model during the Warm-up stage q  A (1+1)EA is used to evolve a controller, optimizing control values q  Fitness is based on the evaluation of the racing line with respect to the track model
  12. 12. GECCO 2013 Simulated Car Racing Competition Ahoora Driver Mohammad reza Bonyadi, Samadhi Nallaperuma, Zbigniew Michalewicz, and Frank Neumann University of Adelaide, Australia
  13. 13. GECCO 2013 Simulated Car Racing Competition Ahoora Driver q  Four main parameterized modules: " Steer controller " Speed controller " Opponent manager " Stuck manager q  Parameters have been set using an evolutionary algorithm (a continuous space evolutionary method) for several tracks with known friction q  During competition, parameters are adapted based on " The estimated friction " Trial and error (adaptively based on number of failures, e.g. out of the track) q  Additional features: jump detection and management (in steer and speed modules)
  14. 14. GECCO 2013 Simulated Car Racing Competition THE GAZELLE - Adaptive Car Pilot Dana Vrajitoru and Kholah Albelihi Indiana University South Bend
  15. 15. GECCO 2013 Simulated Car Racing Competition The GAZELLE Adaptive Car Pilot q  Mainly based on programmed heuristics q  Four modules: " Target direction " Target speed " Opponent detection " Trouble spots handling q  Depending on the current state, the opponent detection module might rise some flag to change the behaviors of other modules
  16. 16. GECCO 2013 Simulated Car Racing Competition GRN Driver Stéphane Sanchez & Sylvain Cussat-Blanc University of Toulouse FRANCE
  17. 17. GECCO 2013 Simulated Car Racing Competition GRN Driver q  A Gene Regulatory Network (GRN) regulates the car steering and throttle " Proteins are encoded in a genome and are evolved by a standard GA (optimization on 3 tracks, normal+mirrored for longest distance) " This approach is naturally adaptative and resistant to noise (no noise filter implemented) q  Scripted recovery behavior and driving assistance (traction control and ABS) q  Modification of the GRN perception to learn braking zones of the track during warm up and to handle opponents during the race Track  sensor  3   Track  sensor  5   Track  sensor  7   Track  sensor  8   Track  sensor  9   Track  sensor  10   Track  sensor  11   Track  sensor  13   Track  sensor  15   Speed  X   Speed  Y   GRN   Le;  steer   Right  steer   Accelerator   Brake   Steer=(le;-­‐right)/(le;+right)   accelbrake=(accel-­‐brake)/(accel+brake)   Normalized  in  [0,1]  
  18. 18. GECCO 2013 Simulated Car Racing Competition Tetsuo Shirakawa, Show Nakamura, and Ruck Thawonmas Intelligent Computer Entertainment Laboratory Ritsumeikan University ICER-IDDFS
  19. 19. GECCO 2013 Simulated Car Racing Competition q  Based on iterative deepening depth-first search for path finder and accelerator control " Select the path having the highest evaluation points " If such a path cannot be found, use a default module implemented according to our understanding (J) of Autopia’s one q  Warm-up " Slow at the first loop to learn the track " Then, try both dirt and road parameters and select the better one " Slow the speed down at every past accident location, if any q  Use simple rules to avoid a crash with another car q  Implement a rule to regain the car’s balance when losing it IDDFS
  20. 20. GECCO 2013 Simulated Car Racing Competition Jan Quadflieg, Tim Delbruegger, Kai Verlage and Mike Preuss TU Dortmund Mr. Racer
  21. 21. GECCO 2013 Simulated Car Racing Competition Mr. Racer 2013 q  Main features " 2 * 28 Parameters learned offline with the CMA-ES " Noise handling with low pass filtering and regression " One parameter set for tarmac tracks, one for dirt tracks " Completely new opponent handling (based on bachelor thesis of Kai Verlage) q  On-line learning during the warm-up " Track model " Choice of parameters set " Tuning of target speed for all corners q  Opponent handiling " A module recommends overtaking lines, blocking lines or target speeds depending on the current situation " Recommendations become more defensive depending on damage " Planning module incorporates recommended target speed and racing line into the plan
  22. 22. Presto AI SCR Competition Entry 2013 Uwe Kadritzke
  23. 23. Presto AI l  Pure Heuristics l  Use of Physical Laws, e.g. Centripetal Force l  Inspired by Bernhard Wymann (BT Driver) l  Unfinished, buggy Main Areas of Attention l  Steering l  Speed Control l  Dealing with Noise
  24. 24. GECCO 2013 Simulated Car Racing Competition SnakeOil Chris X Edwards
  25. 25. GECCO 2013 Simulated Car Racing Competition SnakeOil q  Main goal was to develop a library to encourage Python programmers to enter the SCR. It's quite easy to use to develop your own bot. q  Mapped a complete turn by turn track description. q  Created a route plan for where to be at every point on the track... but that didn't work and probably can't without a heroic effort. It's harder than it first seems. q  Used the track feature map to mark trouble spots and show more caution there while racing. q  Ready to be optimized with evolutionary algorithms
  26. 26. Qualifying
  27. 27. GECCO 2013 Simulated Car Racing Competition Scoring process: Warm-up & Qualifying q  Scoring process involves three tracks: " Alsoujlak (hill track) " Arraias (desert track) " Sancassa (city track) q  The tracks are not distributed with TORCS: " Generated using the Interactive Track Generator for TORCS and Speed Dreams available at: •  http://trackgen.pierlucalanzi.net " The competitors cannot know the tracks q  Each controller raced for 100000 game ticks in the warm-up stage and then its performance is computed in the qualifying stage as the distance covered within 10000 game ticks
  28. 28. Alsoujlak
  29. 29. GECCO 2013 Simulated Car Racing Competition Qualifying: Alsoujlak 0.00   2000.00   4000.00   6000.00   8000.00   10000.00   12000.00   MrRacer   ICER-­‐IDDFS   AUTOPIA   GRNDriver   Presto  AI   SnakeOil   EVOR   GAZELLE   Ahoora  
  30. 30. Arraias
  31. 31. GECCO 2013 Simulated Car Racing Competition Qualifying: Arraias 0   1000   2000   3000   4000   5000   6000   7000   8000   9000   10000   MrRacer   ICER-­‐IDDFS   AUTOPIA   GRNDriver   Presto  AI   SnakeOil   EVOR   GAZELLE   Ahoora  
  32. 32. Sancassa
  33. 33. GECCO 2013 Simulated Car Racing Competition Qualifying: Sancassa 0   2000   4000   6000   8000   10000   12000   MrRacer   ICER-­‐IDDFS   AUTOPIA   GRNDriver   Presto  AI   SnakeOil   EVOR   GAZELLE   Ahoora  
  34. 34. GECCO 2013 Simulated Car Racing Competition Qualifying summary (scores with noise) Competitor Alsoujlak Arraias Sancassa Total AUTOPIA 6 10 10 26 MrRacer 10 5 6 21 ICER-IDDFS 8 4 8 20 GRNDriver 5 8 4 17 SnakeOil 3 6 3 12 Presto AI 4 0 5 9 EVOR 2 2 2 6 GAZELLE 1 3 1 5 Ahoora 0 1 0 1 Evolutionary Approaches
  35. 35. GECCO 2013 Simulated Car Racing Competition Qualifying summary (scores with noise) Competitor Alsoujlak Arraias Sancassa Total AUTOPIA 6 10 10 26 MrRacer 10 5 6 21 ICER-IDDFS 8 4 8 20 GRNDriver 5 8 4 17 SnakeOil 3 6 3 12 Presto AI 4 0 5 9 EVOR 2 2 2 6 GAZELLE 1 3 1 5 Ahoora 0 1 0 1 Removed from the race
  36. 36. Races
  37. 37. GECCO 2013 Simulated Car Racing Competition Three Tracks For each track we run 8 races with different starting grids Each race is scored using the F1 point system (10 to first, 8 to second, 6 to third, …) Two points to the controller with lesser damage Two points for the fastest lap of the race
  38. 38. GECCO 2013 Simulated Car Racing Competition Competitor Alsoujlak Arraias Sancassa Total AUTOPIA 12 13 13 38 MrRacer 9 5.5 6 20.5 ICER-IDDFS 6 5 8 19 GRNDriver 5 5.5 4 14.5 SnakeOil 3 7 3.5 13.5 Presto AI 3 1 5 9 EVOR 3 4 1 8 GAZELLE 1.5 3 2 6.5 Final Results Mr. Racer is the winner of GECCO-2013 SCR
  39. 39. Thank you! SCR Contacts Official Webpage http://scr.geccocompetitions.com Email scr@geccocompetitions.com Google Group http://groups.google.com/group/racingcompetition

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