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Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles

Department of Electrical Engineering, Universidad de Santiago de Chile, Av. Ecuador 3519, Estación Central, Santiago 9170124, Chile
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Sensors 2021, 21(2), 492; https://doi.org/10.3390/s21020492
Received: 15 November 2020 / Revised: 7 January 2021 / Accepted: 8 January 2021 / Published: 12 January 2021
(This article belongs to the Section Intelligent Sensors)
This paper presents the results of the design, simulation, and implementation of a virtual vehicle. Such a process employs the Unity videogame platform and its Machine Learning-Agents library. The virtual vehicle is implemented in Unity considering mechanisms that represent accurately the dynamics of a real automobile, such as motor torque curve, suspension system, differential, and anti-roll bar, among others. Intelligent agents are designed and implemented to drive the virtual automobile, and they are trained using imitation or reinforcement. In the former method, learning by imitation, a human expert interacts with an intelligent agent through a control interface that simulates a real vehicle; in this way, the human expert receives motion signals and has stereoscopic vision, among other capabilities. In learning by reinforcement, a reward function that stimulates the intelligent agent to exert a soft control over the virtual automobile is designed. In the training stage, the intelligent agents are introduced into a scenario that simulates a four-lane highway. In the test stage, instead, they are located in unknown roads created based on random spline curves. Finally, graphs of the telemetric variables are presented, which are obtained from the automobile dynamics when the vehicle is controlled by the intelligent agents and their human counterpart, both in the training and the test track. View Full-Text
Keywords: machine learning; intelligent agents; autonomous vehicle; reinforcement learning; behavioural cloning machine learning; intelligent agents; autonomous vehicle; reinforcement learning; behavioural cloning
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MDPI and ACS Style

Urrea, C.; Garrido, F.; Kern, J. Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles. Sensors 2021, 21, 492. https://doi.org/10.3390/s21020492

AMA Style

Urrea C, Garrido F, Kern J. Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles. Sensors. 2021; 21(2):492. https://doi.org/10.3390/s21020492

Chicago/Turabian Style

Urrea, Claudio, Felipe Garrido, and John Kern. 2021. "Design and Implementation of Intelligent Agent Training Systems for Virtual Vehicles" Sensors 21, no. 2: 492. https://doi.org/10.3390/s21020492

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