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Open AccessArticle

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

Smart Transportation Lab, Pukyong National University, Busan 48513, Korea
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Appl. Sci. 2020, 10(16), 5722; https://doi.org/10.3390/app10165722
Received: 22 July 2020 / Revised: 15 August 2020 / Accepted: 17 August 2020 / Published: 18 August 2020
(This article belongs to the Section Computing and Artificial Intelligence)
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. View Full-Text
Keywords: multiple autonomous vehicles; deep reinforcement learning; proximal policy optimization; simulation of urban mobility (SUMO); flow framework multiple autonomous vehicles; deep reinforcement learning; proximal policy optimization; simulation of urban mobility (SUMO); flow framework
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Quang Tran, D.; Bae, S.-H. Proximal Policy Optimization Through a Deep Reinforcement Learning Framework for Multiple Autonomous Vehicles at a Non-Signalized Intersection. Appl. Sci. 2020, 10, 5722.

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