Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
Author:
Affiliation:
1. Korea University,Department of Electrical and Computer Engineering,Seoul,Republic of Korea,02841
2. Ajou University,Department of Electrical and Computer Engineering,Suwon,Republic of Korea,16499
Funder
National Research Foundation of Korea
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10278505/10278554/10279436.pdf?arnumber=10279436
Reference17 articles.
1. Learning multiagent communication with backpropagation;sukhbaatar;Proc NeurIPS,0
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5. Cooperative Management for PV/ESS-Enabled Electric Vehicle Charging Stations: A Multiagent Deep Reinforcement Learning Approach
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1. Cooperative Multi-UAV Positioning for Aerial Internet Service Management: A Multi-Agent Deep Reinforcement Learning Approach;IEEE Transactions on Network and Service Management;2024-08
2. Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications;IEEE Access;2024
3. Learning-based Cooperative Mobility Control for Autonomous Drone-Delivery;IEEE Transactions on Vehicular Technology;2023
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