Ehsan Sabouni — accepted AI-conference papers

2025

HMARL-CBF – Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems

H M Sabbir Ahmad, Ehsan Sabouni, Alexander Wasilkoff, Param Budhraja, Zijian Guo, Songyuan Zhang +3

NeurIPS 2025poster

We address the problem of safe policy learning in multi-agent safety-critical autonomous systems. In such systems, it is necessary for each agent to meet the safety requirements at all times while also cooperating with other agents to accomplish the task. Toward this end, we propose a safe Hierarchi…

Cited by 0 Source ↗Scholar ↗

2025

Safety Guaranteed Robust Multi-Agent Reinforcement Learning with Hierarchical Control for Connected and Automated Vehicles

Zhili Zhang, H. M. Sabbir Ahmad, Ehsan Sabouni, Yanchao Sun, Furong Huang, Wenchao Li +1

ICRA 2025

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 used approach is learning-based decision-making, such as reinforcement learning (RL). However, most existing safe RL method

Cited by 5 Source ↗Scholar ↗