5 papers
Safe Continuous-time Multi-Agent Reinforcement Learning via Epigraph Form
Xuefeng Wang, Lei Zhang, Henglin Pu +2
Multi-agent reinforcement learning (MARL) has made significant progress in recent years, but most algorithms still rely on a discrete-time Markov Decision Process (MDP) with fixed…
Space-Time-Frequency Synthetic Integrated Sensing and Communication Networks
Henglin Pu, Xuefeng Wang, Lu Su +1
Integrated sensing and communication (ISAC) promises high spectral and power efficiencies by sharing waveforms, spectrum, and hardware across sensing and data links. Yet commercial…
Continuous-Time Value Iteration for Multi-Agent Reinforcement Learning
Xuefeng Wang, Lei Zhang, Henglin Pu +2
Existing reinforcement learning (RL) methods struggle with complex dynamical systems that demand interactions at high frequencies or irregular time intervals. Continuous-time RL (C…
OTFS-ISAC System with Sub-Nyquist ADC Sampling Rate
Henglin Pu, Xuefeng Wang, Ajay Kumar +2
Integrated sensing and communication (ISAC) has emerged as a pivotal technology for next-generation wireless communication and radar systems, enabling high-resolution sensing and h…
DeepSafeMPC: Deep Learning-Based Model Predictive Control for Safe Multi-Agent Reinforcement Learning
Xuefeng Wang, Henglin Pu, Hyung Jun Kim +1
Safe Multi-agent reinforcement learning (safe MARL) has increasingly gained attention in recent years, emphasizing the need for agents to not only optimize the global return but al…