… and open problems in multi-agent reinforcement learning (MARL) research for mobility
scenarios. Chief … This introduces real risk in autonomous mobility scenarios, especially when …

… for controlling (autonomous) MoD systems in the following. For a review of multi-agent DRL,
… further elaborate on how we build on the multi-agent DRL literature in Section 3. Classical …

… multi-agent reinforcement learning (… training quantum reinforcement learning models cannot
be easily extended. Therefore, a new design especially for quantum reinforcement learning …

… , both in terms of traffic flow and individual mobility, as well as from the road safety point of …
autonomous cars could then monopolize the traffic. Using multi-agent reinforcement learning (…

… Thus, this paper proposes a novel air transportation service management algorithm based
on multi-agent deep reinforcement learning (MADRL) to address the challenges of multi-UAM …

… In Multi-Agent (MA) environments, multiple agents execute actions and can affect the states
… extended with DNNs for MA DRL learning, giving rise to Multi-Agent DRL (MADRL). The …

… In this paper, we used Multi-Agent Reinforcement Learning to … Multi-Agent Reinforcement
Learning (MARL) offers a … in design and can learn efficient environment control strategies …

… By applying advanced machine learning techniques to Intelligent Transportation Systems,
we are tackling mobility challenges, promoting safer interactions between AVs and human …

… Monte Carlo Tree Search (MCTS) was proposed to prevent loss of separation for UAS in
an urban air mobility (UAM) setting. A computationally efficient MDP based decentralized …

… Autonomous mobile robots (AMRs) are increasingly being used to enable efficient material
… is proposed that uses multi-agent reinforcement learning, where AMR agents learn to bid on …