Songnam Hong

Separating policy roles in offline RL unlocks significant performance gains by tailoring updates for value bootstrapping versus direct action execution.

Sequential refinements using MPI can enhance offline RL performance beyond traditional behavior constraints, outperforming established baselines.

Achieving up to 99.5% reduction in communication costs while ensuring information-theoretic security could revolutionize federated learning on edge devices.