Seongjin Choi

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

By reframing DODE as a sequential decision-making problem, this approach addresses the credit assignment challenge through a learned policy and provides a novel framework for calibration of microscopic traffic simulations.