Proximal Policy Optimization Through a Deep Reinforcement Learning Framework for Multiple Autonomous Vehicles at a Non-Signalized Intersection

Abstract

Advanced deep reinforcement learning shows promise as an approach to addressing continuous control tasks, especially in mixed-autonomy traffic. In this study, we present a deep reinforcement-learning-based model that considers the effectiveness of leading autonomous vehicles in mixed-autonomy traffic at a non-signalized intersection. This model integrates the Flow framework, the simulation of urban mobility simulator, and a reinforcement learning library. We also propose a set of proximal policy optimization hyperparameters to obtain reliable simulation performance. First, the leading autonomous vehicles at the non-signalized intersection are considered with varying autonomous vehicle penetration rates that range from 10% to 100% in 10% increments. Second, the proximal policy optimization hyperparameters are input into the multiple perceptron algorithm for the leading autonomous vehicle experiment. Finally, the superiority of the proposed model is evaluated using all human-driven vehicle and leading human-driven vehicle experiments. We demonstrate that full-autonomy traffic can improve the average speed and delay time by 1.38 times and 2.55 times, respectively, compared with all human-driven vehicle experiments. Our proposed method generates more positive effects when the autonomous vehicle penetration rate increases. Additionally, the leading autonomous vehicle experiment can be used to dissipate the stop-and-go waves at a non-signalized intersection.