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Published byMartha Watson Modified over 9 years ago
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James Ihrig Awrad Mohammed Ali
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In our project we will use HyperNeat C++ to train a neural network to drive a car using TORCS as a platform. This will be done by sending the Neural Networks generated from HyperNeat and receiving performance data from TORCS.
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We chose to do this project because we are interested in modeling agents in a continuous 3D environment. We chose TORCS that because it is a an open source platform that has been used similarly in the past.
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Connecting TORCS with NEAT is a very popular area it have been done before as we will see here: In 2009 Luigi Cardamone, Daniele Loiacono and Pier Luca Lanzi did a very similar to what we are trying to do in our project. Jorge Munoz, German Gutierrez and Araceli Sanchis create a controller for the game TORCS by learning how another controller or humans play the game and they use NEAT as one of the controllers.
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Communication from HyperNEAT ◦ Node Names ◦ Node Links ◦ Link Weights ◦ Unique ID for the network Communication from TORCS ◦ Lap Times ◦ Accumulated error over lap ◦ Number of checkpoints reached ◦ Unique ID for the network
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Inputs ◦ Offset from center line ◦ Speed of vehicle ◦ Bias Outputs ◦ Steering ◦ Gas Percentage ◦ Brake Percentage
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To calculate fitness we will make use of the number of segments passed, total laptime and the accumulated deviation from the centerline of the track.
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HyperNEAT Range Sensors Pie Slice Sensors
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We will create sensors to detect how far the car from the edges.
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