7 papers · 1 filter
Role-Adaptive Policy Optimization for Offline Reinforcement Learning
Seonvin Cho, Soohyun Choi, Songnam Hong
Policy regularization in offline reinforcement learning balances policy improvement against reliance on uncertain value estimates. This balance can differ between selecting actions…
Online Movable Antenna Repositioning Under Movement Delay and a Long-Term Energy Budget
Yunseob Tae, Jeongjae Lee, Songnam Hong
Movable antenna (MA) systems improve the channel by repositioning antennas, but each repositioning interrupts data transmission for a time set by the largest displacement and consu…
Is One Step Enough for Offline Policy Improvement?
Soohyun Choi, Seonvin Cho, Songnam Hong
Behavior regularization in offline reinforcement learning limits the exploitation of critic errors, but strong anchoring can also restrict policy improvement. We study how policy i…
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…