  In this work, we study vision-based end-to-end reinforcement learning on
vehicle control problems, such as lane following and collision avoidance. Our
controller policy is able to control a small-scale robot to follow the
right-hand lane of a real two-lane road, while its training was solely carried
out in a simulation. Our model, realized by a simple, convolutional network,
only relies on images of a forward-facing monocular camera and generates
continuous actions that directly control the vehicle. To train this policy we
used Proximal Policy Optimization, and to achieve the generalization capability
required for real performance we used domain randomization. We carried out
thorough analysis of the trained policy, by measuring multiple performance
metrics and comparing these to baselines that rely on other methods. To assess
the quality of the simulation-to-reality transfer learning process and the
performance of the controller in the real world, we measured simple metrics on
a real track and compared these with results from a matching simulation.
Further analysis was carried out by visualizing salient object maps.
