  Model-free reinforcement learning has recently been shown to successfully
learn navigation policies from raw sensor data. In this work, we address the
problem of learning driving policies for an autonomous agent in a high-fidelity
simulator. Building upon recent research that applies deep reinforcement
learning to navigation problems, we present a modular deep reinforcement
learning approach to predict the steering angle of the car from raw images. The
first module extracts a low-dimensional latent semantic representation of the
image. The control module trained with reinforcement learning takes the latent
vector as input to predict the correct steering angle. The experimental results
have showed that our method is capable of learning to maneuver the car without
any human control signals.
