Before revision:  Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive environments. The experiments show that the driving agent trained by reinforcement learning on a small fixed set of maps generalizes poorly to unseen maps. We further validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures.
After revision:  Then a range of diverse maps can be assembled from those blocks with procedural generation, which are further turned into interactive training environments where traffic flows of nearby vehicles with realistic kinematics are rendered. We validate that training with the increasing number of procedurally generated maps significantly improves the generalization of the agent across scenarios of different traffic densities and map structures.