10 citations · 17 across the 12 of their papers we have counts for
5 papers · 1 filter
Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation
Soohyun Park, Jae Pyoung Kim, Chanyoung Park +2
For Industry 4.0 Revolution, cooperative autonomous mobility systems are widely used based on multi-agent reinforcement learning (MARL). However, the MARL-based algorithms suffer f…
Multi-Agent Reinforcement Learning for Cooperative Air Transportation Services in City-Wide Autonomous Urban Air Mobility
Chanyoung Park, Gyu Seon Kim, Soohyun Park +2
The development of urban-air-mobility (UAM) is rapidly progressing with spurs, and the demand for efficient transportation management systems is a rising need due to the multifacet…
Coordinated Multi-Agent Reinforcement Learning for Unmanned Aerial Vehicle Swarms in Autonomous Mobile Access Applications
Chanyoung Park, Haemin Lee, Won Joon Yun +2
This paper proposes a novel centralized training and distributed execution (CTDE)-based multi-agent deep reinforcement learning (MADRL) method for multiple unmanned aerial vehicles…
Quantum Multi-Agent Actor-Critic Networks for Cooperative Mobile Access in Multi-UAV Systems
Chanyoung Park, Won Joon Yun, Jae Pyoung Kim +4
This paper proposes a novel algorithm, named quantum multi-agent actor-critic networks (QMACN) for autonomously constructing a robust mobile access system employing multiple unmann…
Multi-Agent Deep Reinforcement Learning for Efficient Passenger Delivery in Urban Air Mobility
Chanyoung Park, Soohyun Park, Gyu Seon Kim +3
It has been considered that urban air mobility (UAM), also known as drone-taxi or electrical vertical takeoff and landing (eVTOL), will play a key role in future transportation. By…