Estimate a neural networks training duration when it is learning to drive a car : Developing a neural network in a small racing game inUnity

University essay from Blekinge Tekniska Högskola/Institutionen för datavetenskap

Abstract: Background. Machine learning technology is used daily in many aspects of computers. Neural network is a machine learning technique. The importance of carsthat are self driving has increased in recent years and the research about it has alsoincreased. Some cars that are produced today already have an autopilot feature inthem. Objectives. In this thesis the objective is to find how the training duration of aneural network changes, when the track becomes harder and harder. Methods. To achieve this a small game and a neural network was developed inUnity. The neural network receive input from 5 sensors and gave 2 outputs, an angleand a velocity. The neural network will be trained in the game on 4 different tracks,each with one more obstacle than the last one. The neural network will generatetwenty cars that will drive simultaneously to the end of the track. If a car collidewith a wall, it gets destroyed. When all cars are destroyed a new generation is generated based on the 2 cars that got destroyed last. Results. The first track was finished in 8,3 seconds, the second track in 28,9 secondsand that is a 248% increase from the first track. The third track was finished in 44,5seconds and that is a 54% increase from the second track. Fourth track were finishedin 39,9 seconds and that was a 10,3% decrease in time. Conclusions. In conclusion the training duration increased from track 1 to track2. from track 2 to track 3 and 4 the increase is presumed to be because the trackis larger and because of the speed output from the neural network. The trainingduration does change, but the change corresponds with the change of the length ofthe track.

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