Improving the generalizability and robustness of large-scale traffic signal control

Understand

A number of deep reinforcement-learning (RL) approaches propose to control traffic signals.

• In this work, we study the robustness of such methods along two axes.
• First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data.
• Second, we provide a more systematic study of the generalization ability of RL methods to new networks with different traffic regimes.