This work studies how policy improvement is composed through multi-step proximal policy improvement (MPI), which re-centers each proximal objective on the preceding policy, and identifies improvement composition as a design choice alongside regularization strength, with distinct effects from horizon subdivision and add...
Role-Adaptive Policy Optimization (RAPO), which adapts policy-update coefficients according to their roles in value learning and execution, is proposed, which shows improvements over both base algorithms.
Energy-efficient control in dense wireless networks requires jointly optimizing user association, scheduling, and base station (BS) activation under finite energy budgets. Existing Lyapunov-based approaches enforce energy constraints only asymptotically. Under finite-horizon operation, the myopic per-slot drift-plus-pe...
Wonhyeok Ko, Yeonseo Jeong, Sungweon Hong et al.· IEEE Internet of Things Jour...· 1 citation
This work proposes a lightweight and information-theoretically secure aggregation framework that securely computes the majority vote (MV) polynomial through single-round secure multiplication, ensuring end-to-end information-theoretic security under the honest-majority assumption while revealing only the final aggregat...
Robust reinforcement learning (RRL) aims to develop a robust policy that maintains stable performance across diverse environments characterized by an uncertainty set. This set consists of perturbed environments derived from a nominal (training) environment that generates samples, thereby capturing potential discrepanci...
Ukjo Hwang, Songnam Hong· IEEE Transactions on Neural...· 0 citations
Offline reinforcement learning (RL) must reconcile two competing requirements: policy updates should stay near dataset-supported actions to keep value estimates reliable, yet meaningful gains often require moving beyond the behavior distribution. We develop a geometric view of offline actor updates by modeling policies...
The proposed PathBridger is a hierarchical offline GCRL method that explicitly connects subgoal selection to short-horizon execution, and constructs a state-space bridge toward the selected intermediate endpoint and decodes it into a short executable action chunk using an inverse dynamics model.
Simulations show that HeLyMARL is the only method that sustains the throughput-fairness balance together with uninterrupted service throughout the horizon, outperforming conventional MARL, Lyapunov-based, and constrained MARL benchmarks without premature budget exhaustion.
Yeonseo Jeong, Wonhyeok Ko, Sungweon Hong et al.· 0 citations
This work proposes a lightweight and information-theoretically secure aggregation framework that securely computes the majority vote (MV) polynomial through single-round secure multiplication, ensuring end-to-end information-theoretic security under the honest-majority assumption while revealing only the final aggregat...
