… Overall, we observe that using our proposed decentralized multi-agent learning scheme,
we are able to induce altruism into the decision-making process of autonomous vehicles and …

… To address the above issues, we develop a multi-agent reinforcement learning algorithm
by employing a multi-agent advantage actor-critic network (MA2C) for multi-AV lane-changing …

… , and autonomous cars could then monopolize the traffic. Using multi-agent reinforcement
… , researchers have attempted to design autonomous vehicles for both scenarios, and this …

… incorporation of nonautonomous vehicles (FCFS-light) [2], [35], as well as emergency vehicles
such as ambulances or police cars (FCFS-EMERG) [36]. The advantages offered by FCFS …

… Autonomous vehicles (AVs) play a pivotal role in this transformation, with the potential to …
This study introduces a novel Multi-Agent Actor–Critic (MA2C) algorithm tailored for multi-AV …

… Furthermore, the study settings used in this paper are the standard paradigm associated
with multi-agent planning: Centralized learning but with decentralized execution (Kraemer & …

… We propose a multi-agent based modeling of a C-ITS, that … smooth cooperation between non
cooperative and cooperative … We present our multi-agent model, tested through simulations …

… architecture for multi-agent autonomous driving, which incorporated a selfsupervised attention
encoder module to process limited communication information. With driving policy trained …

… multi-agent highway driving simulator we developed as part of this work. We present
techniques (MA-MeSN-MM) to derive a cooperative … driving simulator and OpenAI’s multi-agent …

… In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (…
the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-…