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The 2010 Simulated Car Racing Championship @ GECCO-2010Daniele Loiacono, Luigi Cardamone, Martin V. Butz, and Pier Luca La...
2010 Simulated Car Racing Championship9 races during 3 conferencesACM GECCO-2010, Portland, OR (USA), July 7-IEEE WCCI-201...
What is the structure of a race?<br />Three stages: warm up, qualifiers, actual race <br />During warm-up, each driver can...
Motivations<br />Proposing a relevant game-based competition<br />more representative of commercial games AI<br />more sim...
What’s new?<br />If everything seems under control, you're not going fast enough — Mario Andretti <br />Warm-up stage<br /...
The Open Racing Car Simulator<br />
The Open Racing Car Simulator<br />TORCS is a state of the art open source simulator written in C++<br />Main features<br ...
The Open Racing Car Simulator & the Competition Software<br />TORCS<br />TORCS<br />PATCH<br />BOT<br />BOT<br />BOT<br />...
Separates the bots from TORCS
Build a well-defined sensor model
Works in real-time</li></ul>UDP<br />UDP<br />UDP<br />BOT<br />BOT<br />BOT<br />
Sensors and actuators<br />Rangefinders for edges on the track and opponents <br />Speed, RPM, fuel, damage, angle with tr...
Competitors<br />
The competitors<br />Four entries submitted to this first leg<br />AUTOPIA, E. Onieva, Consejo Superior de Investigaciones...
AUTOPIA<br />Enrique Onieva Caracuel<br />Industrial Computer Science Department.<br />Centro de Automática y Robótica <br...
Architecture Schema<br />Three basic modules for gear, steering and speed control<br />Steady stage genetic algorithm to c...
Opponents Management (Steering)<br />If O(-20,-10,10,20) detects an opponent with lateral displacement less than 20 meters...
Learning Module (Warm-Up)<br />Running normally in warm-up stage.<br />Maintain a vector with as many real values as the t...
Mr Racer<br />Susanna Pohl, Jan Quadflieg and Tim DelbrüggerTU Dortmund<br />
Mr. Racer 2009-2010<br />Mr Racer 2009<br />Good classifier which identifies six situations<br />Acceleration/brake learne...
Mr. Racer - Classification<br />Angle based measure mapped to six situations (straight, fast corner, slow corner, etc)<br />
Mr. Racer 2010 – The catch<br />Noise completely breaks our classifier<br />Without a descent classifier we can‘t learn th...
Jorge Muñoz Fuentes<br />Department of Computer Science<br />Carlos III University of Madrid<br />
Jorge Muñoz<br />Build a model of the track during the warm-up stage.<br />Two neural networks to predict the trajectory u...
Jorge Muñoz<br />Other optimizations performed during the warm-up and used in the race:<br />The car remember where it goe...
Joseph Alton<br /><ul><li>Joseph Alton</li></li></ul><li>Joseph Alton<br />Bot made specially for the oval leg, and is onl...
COBOSTAR<br />Thies Lönneker and Martin V. Butz<br />University of Würzburg<br />http://www.coboslab.psychologie.uni-wuerz...
CIG-2008 Champ<br />Luigi Cardamone<br />Politecnico di Milano<br />
Qualifying<br />
Scoring process: Warm-up Qualifying<br />Scoring process involves three motor high-speedway tracks:<br />Kiwi<br />Mango<b...
Qualifying:  Kiwi<br />
Qualifying:  Mango<br />
Qualifying:  Triangle<br />
Qualifyng summary<br />
What about qualifying? <br />COBOSTAR is the fastest driver (despite not optimized neither for high-speedways nor for nois...
The GECCO-2010 Leg<br />
The Three GPs<br />For each track we run 5 races with random starting grids <br />Each race is scored using the F1 point s...
Final Results<br />
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2010 Simulated Car Racing Championship @ GECCO-2010

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2010 Simulated Car Racing Championship @ GECCO-2010

  1. 1. The 2010 Simulated Car Racing Championship @ GECCO-2010Daniele Loiacono, Luigi Cardamone, Martin V. Butz, and Pier Luca Lanzi<br />
  2. 2. 2010 Simulated Car Racing Championship9 races during 3 conferencesACM GECCO-2010, Portland, OR (USA), July 7-IEEE WCCI-2010, Barcelona (Spain), July 18-23IEEE CIG-2010, Copenhagen (Denmark), August 18–21<br />Develop a driver for TORCS(hand-coded, learned, evolved, …) <br />Drivers will be awarded based on their score in each conference competition<br />At the end, the team with highest overall scorewins the championship<br />2<br />
  3. 3. What is the structure of a race?<br />Three stages: warm up, qualifiers, actual race <br />During warm-up, each driver can explore the track and learn something useful <br />During qualifiers, each driver races alone against the clock (the best 8 drivers move to the race)<br />During the race all the drivers race together<br />3<br />
  4. 4. Motivations<br />Proposing a relevant game-based competition<br />more representative of commercial games AI<br />more similar to a real-world problem <br />Proposing a funny and exciting competition<br />you can see and play with the entries of this competition<br />human players can interact with AI<br />a lot of programmed AI available for comparison<br />Proposing a challenging competition<br />not designed with Machine Learning in mind<br />computationally expensive<br />real-time<br />dealing with a lot of practical issues<br />
  5. 5. What’s new?<br />If everything seems under control, you're not going fast enough — Mario Andretti <br />Warm-up stage<br />Before qualifying stage, competitors have 100000 game ticks to race on the track<br />Allows track learning and optimization of parameters<br />Noisy sensors<br />Track sensors and opponent sensors are affected by a Gaussian noise (standard deviation equal to 10% of the readings)<br />Extended sensor model<br />Focus sensors<br />Z position and speed<br />Direction of track sensors fully customizable<br />Added clutch control and focus command<br />
  6. 6. The Open Racing Car Simulator<br />
  7. 7. The Open Racing Car Simulator<br />TORCS is a state of the art open source simulator written in C++<br />Main features<br />Sophisticated dynamics<br />Provided with several cars, tracks, and controllers<br />Active community of users and developers<br />Easy to develop your own controller <br />OS Support<br />Linux: binaries and building from sources <br />Windows: binaries and “limited” bulding from sources support<br />OSX: legacy binaries and no building from sources support <br />
  8. 8. The Open Racing Car Simulator & the Competition Software<br />TORCS<br />TORCS<br />PATCH<br />BOT<br />BOT<br />BOT<br />SBOT<br />SBOT<br />SBOT<br /><ul><li>The competition server
  9. 9. Separates the bots from TORCS
  10. 10. Build a well-defined sensor model
  11. 11. Works in real-time</li></ul>UDP<br />UDP<br />UDP<br />BOT<br />BOT<br />BOT<br />
  12. 12. Sensors and actuators<br />Rangefinders for edges on the track and opponents <br />Speed, RPM, fuel, damage, angle with track, distance race, position on track, etc.<br />Four effectors: steering wheel [-1,+1], gas pedal [0, +1], Brake pedal [0,+1], Gearbox {-1,0,1,2,3,4,5,6}<br />
  13. 13. Competitors<br />
  14. 14. The competitors<br />Four entries submitted to this first leg<br />AUTOPIA, E. Onieva, Consejo Superior de Investigaciones Científicas, Madrid<br />J. Muñoz, Carlos III University of Madrid<br />S.Pohl, J. Quadflieg and T. Delbrügger, TU Dortmund <br />Joseph Alton, University of Birmingham <br />Two more entries from the 2009 championship<br />COBOSTAR (T. Lönneker & M.V. Butz, University of Würzburg)<br />POLIMI (Cardamone, Politecnico di Milano) <br />
  15. 15. AUTOPIA<br />Enrique Onieva Caracuel<br />Industrial Computer Science Department.<br />Centro de Automática y Robótica <br />Consejo Superior de Investigaciones Científicas<br />enrique.onieva@car.upm-csic.es<br />
  16. 16. Architecture Schema<br />Three basic modules for gear, steering and speed control<br />Steady stage genetic algorithm to compute the best weights to combine parameters for steering and target speed control<br />Opponents module <br />Acts on steering and brake signal to overtake opponents and avoid collisions<br />Learning Module in Warm-up Stage<br />Factors over the target speed in certain track segments<br />
  17. 17. Opponents Management (Steering)<br />If O(-20,-10,10,20) detects an opponent with lateral displacement less than 20 meters, move the steering trackwidth/100 through the direction with more free distance.<br />If O(-40,-30,-20,-10) detects an opponent with lateral displacement less than 20 meters, move the steering trackwidth/50 through the right.<br />If O(-70,-60,-50)<15 meters, move the steering trackwidth/50 through the right.<br />If O(-140,-130,-120,-110,-100,-90,-80)<15meters , move the steering trackwidth/50 through the right.<br />Mirroring actions for positive sensors<br />O(X)  Opponent Measure of Sensor Oriented at X<br />Steering Modification limited to 0.35<br />
  18. 18. Learning Module (Warm-Up)<br />Running normally in warm-up stage.<br />Maintain a vector with as many real values as the track length in meters.<br />Vector initialized to 1.0<br />If the vehicle goes out of the track or suffers damage then multiply vector positions from 100 meters before the current position by 0.9.<br />
  19. 19. Mr Racer<br />Susanna Pohl, Jan Quadflieg and Tim DelbrüggerTU Dortmund<br />
  20. 20. Mr. Racer 2009-2010<br />Mr Racer 2009<br />Good classifier which identifies six situations<br />Acceleration/brake learned offline using an EA <br />Model of the track learned online<br />Simple heuristic to use the model: override the learned behaviour on straights and in full speed corners to drive flat out<br />Mr Racer 2010<br />Save the model after warmup, use it during qualifying and the race<br />Use the model to derive a plan consisting of target speeds and a racing line <br />Optimize the parameter set of the planning module, left to be done for the next round<br />
  21. 21. Mr. Racer - Classification<br />Angle based measure mapped to six situations (straight, fast corner, slow corner, etc)<br />
  22. 22. Mr. Racer 2010 – The catch<br />Noise completely breaks our classifier<br />Without a descent classifier we can‘t learn the track<br />Without a trackmodel we can‘t drive <br />Workaround for GECCO-2010: Classify the whole track as being straight<br />New classifier for the next leg at WCCI-2010<br />
  23. 23. Jorge Muñoz Fuentes<br />Department of Computer Science<br />Carlos III University of Madrid<br />
  24. 24. Jorge Muñoz<br />Build a model of the track during the warm-up stage.<br />Two neural networks to predict the trajectory using the track model. Two neural networks to predict the target speed given the model of the track and the current car position<br />The four neural networks are trained with backpropagation using data retrieved from a human player. <br />The controller tries to imitate a human player.<br />A scripted policy is used to follow the trajectory (steering value), set the speed (accelerate and brake values), set the clutch and the gear<br />
  25. 25. Jorge Muñoz<br />Other optimizations performed during the warm-up and used in the race:<br />The car remember where it goes out of the car or drives far form the trajectory and in the next laps goes slower in those points<br />The car remember where it follows the trajectory perfectly and tries to go faster in the next laps.<br />Overtaking is made by means of modifying the predicted trajectory, the modification is bigger in straighs than in turns<br />To avoid overtakin the car also modifies the trajectoy, trying to stay in front of the opponent car. <br />
  26. 26. Joseph Alton<br /><ul><li>Joseph Alton</li></li></ul><li>Joseph Alton<br />Bot made specially for the oval leg, and is only designed to be competent on this leg.<br />Plan to change and improve the bot as I learn and to meet the demands of the other tracks<br />Warm-up Learning<br />Car goes slowly around the track recording in each segment (e.g. 0 metre, 1 metre, 2 metre) whether it is turning or not<br />This information can then be loaded up in qualifying or finals to help find a more appropriate target speed.<br />Straight = fast, Turn = slower<br />During the race, the driver looks ahead to the next or the next two segments depending on the speed<br />
  27. 27. COBOSTAR<br />Thies Lönneker and Martin V. Butz<br />University of Würzburg<br />http://www.coboslab.psychologie.uni-wuerzburg.de<br />
  28. 28. CIG-2008 Champ<br />Luigi Cardamone<br />Politecnico di Milano<br />
  29. 29. Qualifying<br />
  30. 30. Scoring process: Warm-up Qualifying<br />Scoring process involves three motor high-speedway tracks:<br />Kiwi<br />Mango<br />Triangle<br />Kiwi is a (renamed and slightly modified) track of TORCS<br />Mango and Triangle has been developed by the organizers<br />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 <br />
  31. 31. Qualifying: Kiwi<br />
  32. 32. Qualifying: Mango<br />
  33. 33. Qualifying: Triangle<br />
  34. 34. Qualifyng summary<br />
  35. 35. What about qualifying? <br />COBOSTAR is the fastest driver (despite not optimized neither for high-speedways nor for noisy sensors) <br />Experiments without noise shows that <br />Noise is not critical in high-speedways for driving alone<br />Some controllers (MR. Racer and Muñoz) perform even better with noise<br />
  36. 36. The GECCO-2010 Leg<br />
  37. 37. The Three GPs<br />For each track we run 5 races with random starting grids <br />Each race is scored using the F1 point system (10 to first, 8 to second, 6 to third, …)<br />Two points to the controller with lesser damage<br />Two points for the fastest lap of the race<br />35<br />
  38. 38. Final Results<br />
  39. 39. OnievaWINner of the SIMULATED <br />Car racing competition @ GECCO-2010<br />Enrique Onieva<br />
  40. 40. What about the race? <br />Results do not confirm the qualifying performance <br />COBOSTAR is the worst performing controller!<br />The results suggest that noise is much more relevant for the opponent perception that for driving alone<br />Races without noise show that AUTOPIA and COBOSTAR being the top controllers (as expected from qualifyng)<br />Cardamone’s controller seems slighlty more reliable (as it is completely based on a Neural Network)<br />

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