  Reinforcement Learning (RL) aims at learning an optimal behavior policy from
its own experiments and not rule-based control methods. However, there is no RL
algorithm yet capable of handling a task as difficult as urban driving. We
present a novel technique, coined implicit affordances, to effectively leverage
RL for urban driving thus including lane keeping, pedestrians and vehicles
avoidance, and traffic light detection. To our knowledge we are the first to
present a successful RL agent handling such a complex task especially regarding
the traffic light detection. Furthermore, we have demonstrated the
effectiveness of our method by winning the Camera Only track of the CARLA
challenge.
