Autonomous vehicle decision and control through reinforcement learning
  with traffic flow randomization
Yuan Lin Antai Xie Xiao Liu
Most of the current studies on autonomous vehicle decision-making and control
tasks based on reinforcement learning are conducted in simulated environments.
The training and testing of these studies are carried out under rule-based
microscopic traffic flow, with little consideration of migrating them to real
or near-real environments to test their performance. It may lead to a
degradation in performance when the trained model is tested in more realistic
traffic scenes. In this study, we propose a method to randomize the driving
style and behavior of surrounding vehicles by randomizing certain parameters of
the car-following model and the lane-changing model of rule-based microscopic
traffic flow in SUMO. We trained policies with deep reinforcement learning
algorithms under the domain randomized rule-based microscopic traffic flow in
freeway and merging scenes, and then tested them separately in rule-based
microscopic traffic flow and high-fidelity microscopic traffic flow. Results
indicate that the policy trained under domain randomization traffic flow has
significantly better success rate and calculative reward compared to the models
trained under other microscopic traffic flows.