Real-time system optimal traffic routing under uncertainties -- Can physics models boost reinforcement learning?

TL;DR

This paper introduces TransRL, a novel reinforcement learning algorithm that integrates physics models to improve real-time traffic routing under uncertainty, achieving better adaptability, reliability, and interpretability in large transportation networks.

Contribution

TransRL combines physics-based deterministic policies with reinforcement learning, guiding learning with a differentiable teacher policy to enhance performance and interpretability in traffic routing.

Findings

TransRL outperforms traditional RL algorithms like PPO and SAC.

TransRL demonstrates superior adaptability and reliability in large networks.

Physics model integration improves interpretability of traffic routing policies.

Abstract

System optimal traffic routing can mitigate congestion by assigning routes for a portion of vehicles so that the total travel time of all vehicles in the transportation system can be reduced. However, achieving real-time optimal routing poses challenges due to uncertain demands and unknown system dynamics, particularly in expansive transportation networks. While physics model-based methods are sensitive to uncertainties and model mismatches, model-free reinforcement learning struggles with learning inefficiencies and interpretability issues. Our paper presents TransRL, a novel algorithm that integrates reinforcement learning with physics models for enhanced performance, reliability, and interpretability. TransRL begins by establishing a deterministic policy grounded in physics models, from which it learns from and is guided by a differentiable and stochastic teacher policy. During training, TransRL aims to maximize cumulative rewards while minimizing the Kullback Leibler (KL) divergence between the current policy and the teacher policy. This approach enables TransRL to simultaneously leverage interactions with the environment and insights from physics models. We conduct experiments on three transportation networks with up to hundreds of links. The results demonstrate TransRL's superiority over traffic model-based methods for being adaptive and learning from the actual network data. By leveraging the information from physics models, TransRL consistently outperforms state-of-the-art reinforcement learning algorithms such as proximal policy optimization (PPO) and soft actor critic (SAC). Moreover, TransRL's actions exhibit higher reliability and interpretability compared to baseline reinforcement learning approaches like PPO and SAC.