Nihat Ay

Well-Posed KL-Regularized Control via Wasserstein and Kalman–Wasserstein KL Divergences

Kullback-Leibler divergence (KL) regularization is widely used in reinforcement learning, but it becomes infinite under support mismatch and can degenerate in low-noise limits. Utilizing a unified information-geometric framework we introduce (Kalman)-Wasserstein-based KL analogues by replacing the F…