14 papers
Multi-step Proximal Policy Improvement in Offline Reinforcement Learning
Soohyun Choi, Seonvin Cho, Songnam Hong
Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near dataset-supported actions to keep value estimates reliable, yet meani…
PathBridger: Subgoal Bridges for Offline Goal-Conditioned Reinforcement Learning
Soohyun Choi, Seonvin Cho, Songnam Hong
Offline goal-conditioned reinforcement learning (GCRL) aims to learn policies for reaching diverse goals entirely from fixed trajectory data. Long-horizon offline GCRL remains chal…
Heterogeneous Multi-Agent Reinforcement Learning for Radio Resource Management under Coupled Finite-Horizon Constraints
Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong +1
Maximizing throughput under proportional fairness in dense wireless networks requires jointly managing user association, scheduling, base station (BS) activation, and handover cont…
Scalable Rate-Splitting Precoding via Recurrent Structure-Preserving Graph Neural Networks
Wonseok Choi, Jeongjae Lee, Songnam Hong
Graph neural network (GNN)-based precoding has demonstrated strong potential for scalable multi-user beamforming in multi-user multiple-input single-output (MU-MISO) systems under…
Information-Theoretically Secure Aggregation for Lightweight Federated Learning: Resilient to Dropouts and Adversaries
Hyeong-Gun Joo, Songnam Hong, Dong-Joon Shin
On-device federated learning (FL) enables privacy-preserving and personalized model training on resource-constrained devices such as smartphones and IoT nodes. To reduce communicat…
CSIT-Free Beamforming for Multi-Group Multicast in Overloaded mmWave Systems
Wonseok Choi, Jeongjae Lee, Songnam Hong
We study downlink multi-group multicast (MGM) transmission in overloaded millimeter-wave (mmWave) systems, where the number of users exceeds the number of transmit antennas. We fir…