End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
This addresses the problem of autonomous urban driving for robotics and AI applications, representing a significant advancement rather than an incremental improvement.
The authors tackled urban driving using a novel reinforcement learning technique called implicit affordances, achieving success in complex tasks like traffic light detection and winning the Camera Only track of the CARLA challenge.
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.