2013 Simulated Car Racing @ GECCO-2013
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2013 Simulated Car Racing @ GECCO-2013

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Simulated Car Racing Competition held during GECCO-2013 ...

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

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2013 Simulated Car Racing @ GECCO-2013 Presentation Transcript

  • 1. GECCO 2013 Simulated Car Racing Competition GECCO 2013 Simulated Car Racing Competition Daniele Loiacono and Pier Luca Lanzi
  • 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. Competition Framework
  • 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. 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. Competitors
  • 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. 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. 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. GECCO 2013 Simulated Car Racing Competition EVOR (Evolutionary Racer) Samadhi Nallaperuma, Frank Naumann (supervisor) University of Adelaide, Austrailia
  • 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. GECCO 2013 Simulated Car Racing Competition Ahoora Driver Mohammad reza Bonyadi, Samadhi Nallaperuma, Zbigniew Michalewicz, and Frank Neumann University of Adelaide, Australia
  • 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. GECCO 2013 Simulated Car Racing Competition THE GAZELLE - Adaptive Car Pilot Dana Vrajitoru and Kholah Albelihi Indiana University South Bend
  • 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. GECCO 2013 Simulated Car Racing Competition GRN Driver Stéphane Sanchez & Sylvain Cussat-Blanc University of Toulouse FRANCE
  • 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. GECCO 2013 Simulated Car Racing Competition Tetsuo Shirakawa, Show Nakamura, and Ruck Thawonmas Intelligent Computer Entertainment Laboratory Ritsumeikan University ICER-IDDFS
  • 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. GECCO 2013 Simulated Car Racing Competition Jan Quadflieg, Tim Delbruegger, Kai Verlage and Mike Preuss TU Dortmund Mr. Racer
  • 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. Presto AI SCR Competition Entry 2013 Uwe Kadritzke
  • 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. GECCO 2013 Simulated Car Racing Competition SnakeOil Chris X Edwards
  • 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. Qualifying
  • 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. Alsoujlak
  • 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. Arraias
  • 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. Sancassa
  • 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. 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. 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. Races
  • 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. 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. Thank you! SCR Contacts Official Webpage http://scr.geccocompetitions.com Email scr@geccocompetitions.com Google Group http://groups.google.com/group/racingcompetition