  Recent research has shown that map raw pixels from a single front-facing
camera directly to steering commands are surprisingly powerful. This paper
presents a convolutional neural network (CNN) to playing the CarRacing-v0 using
imitation learning in OpenAI Gym. The dataset is generated by playing the game
manually in Gym and used a data augmentation method to expand the dataset to 4
times larger than before. Also, we read the true speed, four ABS sensors,
steering wheel position, and gyroscope for each image and designed a mixed
model by combining the sensor input and image input. After training, this model
can automatically detect the boundaries of road features and drive the robot
like a human. By comparing with AlexNet and VGG16 using the average reward in
CarRacing-v0, our model wins the maximum overall system performance.
