Jianbang Liu

Sparse Ergodic Control with Control-Dependent Noise via Physics-Informed Neural Networks

To address the discontinuous ℓ0-type sparsity penalty, smooth non-convex sparsity approximations are introduced that preserve differentiability while retaining sparse threshold behavior and characterize a quasi-threshold sparse structure of the resulting optimal feedback policies in non-affine stochastic systems with control-dependent noise.