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