7 papers
A Mixed-Reality Testbed for Autonomous Vehicles
H. M. Sabbir Ahmad, Ehsan Sabouni, Emrullah Celik +4
We propose a mixed-reality, hardware-in-the-loop (HIL) testbed for autonomous vehicles that seamlessly integrates a physical testbed of mobile robots with a high-fidelity simulatio…
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…
SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning
Zijian Guo, Ä°lker IÅık, H. M. Sabbir Ahmad +1
Specification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal…
Multi-Robot Multi-Queue Control via Exhaustive Assignment Actor-Critic Learning
Mohammad Merati, H. M. Sabbir Ahmad, Wenchao Li +1
We study online task allocation for multi-robot, multi-queue systems with asymmetric stochastic arrivals and switching delays. We formulate the problem in discrete time: each locat…
One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learning
Zijian Guo, Ä°lker IÅık, H. M. Sabbir Ahmad +1
Generalizing to complex and temporally extended task objectives and safety constraints remains a critical challenge in reinforcement learning (RL). Linear temporal logic (LTL) offe…
Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systems
H. M. Sabbir Ahmad, Ehsan Sabouni, Alexander Wasilkoff +6
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…