Integrating Supervised and Reinforcement Learning for Heterogeneous Traffic Simulation

Yasin Maan Yousif, Jörg P. Müller

Abstract: Traffic simulation is needed for planning safe routes of self-driving cars and in analyzing traffic situations of a given area. Commonly supervised learning methods of vehicle, bicycle, and pedestrian traffic models have several limitations such as drifting errors and weak generalization to novel scenarios. Reinforcement learning can address these issues but it is much slower to converge due to the large state and action spaces involved in real-world traffic.

External IDs:dblp:conf/paams/YousifM24a