4 papers
Robust and Safe Multi-Agent Reinforcement Learning with Communication for Autonomous Vehicles: From Simulation to Hardware
Keshawn Smith, Zhili Zhang, H M Sabbir Ahmad +5
Deep multi-agent reinforcement learning (MARL) has been demonstrated effectively in simulations for multi-robot problems. For autonomous vehicles, the development of vehicle-to-veh…
YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
Yuan Zhuang, Yi Shen, Zhili Zhang +2
Advancements in deep multi-agent reinforcement learning (MARL) have positioned it as a promising approach for decision-making in cooperative games. However, it still remains challe…
Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles
Zhili Zhang, H M Sabbir Ahmad, Ehsan Sabouni +4
We address the problem of coordination and control of Connected and Automated Vehicles (CAVs) in the presence of imperfect observations in mixed traffic environment. A commonly use…
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
Han Wang, Sihong He, Zhili Zhang +2
We explore a Federated Reinforcement Learning (FRL) problem where agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work…