… , and autonomous cars could then monopolize the traffic. Using multi-agent reinforcement
learning (MARL) algorithms, researchers have attempted to design autonomous vehicles for …

… In this paper we apply deep reinforcement learning to the problem of forming long term
driving strategies. We note that there are two major challenges that make autonomous driving …

… 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 …

… were presented that allowed the incorporation of nonautonomous vehicles (FCFS-light) [2],
[35], as well as emergency vehicles such as ambulances or police cars (FCFS-EMERG) [36]. …

… We provide a taxonomy of multi-agent learning environments … in categorizing various
autonomous driving problems that can … -Gym, a Multi-Agent Connected, Autonomous Driving …

… Multi-Agent Reinforcement Learning In this section we present a background on multi-agent
reinforcement … In this work we consider a general sum multi-agent stochastic game G which …

… based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow
of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (…

… (vehicles) and the fast‐growing joint action space associated with multi‐agent driving tasks
… Therefore, we present a novel deep reinforcement learning‐based algorithm that combines …

… -source reinforcement learning environment designed for testing and developing autonomous
driving agents for multi-agent … to use with existing reinforcement learning libraries such as …

… in the control algorithms of connected and autonomous vehicle (CAV), it may be … based
multi-agent CAV control model to operate in mixed traffic (both CAVs and human-driven vehicles (…