Reinforcement-Learning-Based Job-Shop Scheduling for Intelligent Intersection Management

The goal of intersection management is to organize vehicles to pass the intersection safely and efficiently. Due to the technical advance of connected and autonomous vehicles, intersection management becomes more intelligent and potentially unsignalized. In this paper, we propose a reinforcement-learning-based methodology to train a centralized intersection manager. We define the intersection scheduling problem with a graph-based model and transform it to the job-shop scheduling problem (JSSP) with additional constraints. To utilize reinforcement learning, we model the scheduling procedure as a Markov decision process (MDP) and train the agent with the proximal policy optimization (PPO). A grouping strategy is also developed to apply the trained model to streams of vehicles. Experimental results show that the learning-based intersection manager is especially effective with high traffic densities. This paper is the first work in the literature to apply reinforcement learning on the graph-based intersection model. The proposed methodology can flexibly deal with any conflicting scenario and indicate the applicability of reinforcement learning to intelligent intersection management.