Multi-Agent Reinforcement Learning for Autonomous On Demand Vehicles

In this study, we elaborate the procedure of designing a supervisory controller for the Autonomous Transit on Demand Vehicle (ATODV) system. Reinforcement learning is implemented to reduce the mean waiting time of the passengers, and a cost function is introduced to penalize the energy consumption o...

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Bibliographic Details
Published inIEEE Intelligent Vehicles Symposium pp. 1461 - 1468
Main Authors Boyali, Ali, Hashimoto, Naohisa, John, Vijay, Acarman, Tankut
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2019
Subjects
Decision making
Electric vehicles
Elevators
Energy consumption
Intelligent vehicles
Neural networks
Q-learning
Random variables
Stochastic processes
Time factors
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Summary:In this study, we elaborate the procedure of designing a supervisory controller for the Autonomous Transit on Demand Vehicle (ATODV) system. Reinforcement learning is implemented to reduce the mean waiting time of the passengers, and a cost function is introduced to penalize the energy consumption of the electric vehicles. A stochastic simulation environment for an ATODV pilot project is coded in the Python environment to train the autonomous cart decision process as agents with artificial intelligence. Passenger group behavior, get-on and get-off times, destinations are modeled as random variables. A single Deep Q-Learning Network is trained subject to multi-agent settings. The ATODV system's independent decision making for the carts to reduce the passenger's waiting time while constraining the energy consumption and empty vehicle motion is evaluated.
ISSN:2642-7214
DOI:10.1109/IVS.2019.8813876