Understanding Deep Reinforcement Learning Algorithm in Typical Ramp Metering Scenarios

Abstract:

This paper presents the control mechanism of deep reinforcement learning （DRL） in a typical ramp metering scenario. The state value function is used to evaluate if the DRL model has the ability to distinguish the change of state. The saliency map is used to perceive the state key features and control pattern for the DRL model under specific traffic states. By using the input perturbation， the action match ratio and control performance under perturbed data are analyzed to explore the key areas of control. The results show that the DRL model can evaluate the traffic state accurately， distinguish the key features， and then make reasonable decisions.