  We present research using the latest reinforcement learning algorithm for
end-to-end driving without any mediated perception (object recognition, scene
understanding). The newly proposed reward and learning strategies lead together
to faster convergence and more robust driving using only RGB image from a
forward facing camera. An Asynchronous Actor Critic (A3C) framework is used to
learn the car control in a physically and graphically realistic rally game,
with the agents evolving simultaneously on tracks with a variety of road
structures (turns, hills), graphics (seasons, location) and physics (road
adherence). A thorough evaluation is conducted and generalization is proven on
unseen tracks and using legal speed limits. Open loop tests on real sequences
of images show some domain adaption capability of our method.
