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2010 Simulated Car Racing Championship @ GECCO-2010
 

2010 Simulated Car Racing Championship @ GECCO-2010

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    2010 Simulated Car Racing Championship @ GECCO-2010 2010 Simulated Car Racing Championship @ GECCO-2010 Presentation Transcript

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