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.

本文提出了使用最新的强化学习算法进行端到端驾驶研究，仅使用来自前置摄像头的 RGB 图像，通过异步演员 - 评论家 (A3C) 框架在真实的赛车游戏中学习车辆控制，并在不同的道路结构、图形和物理特性下进行评估。结果表明本方法可以快速收敛和更稳健的驾驶，同时在看不见的赛道上和法定车速下进行了广义化证明。同时，我们的方法在实际影像序列上显示出一定的领域适应能力。