Interleaved Projected Gradient Descent for Safe Imitation Learning

Excerpt

arXiv:2610.07167v1 Announce Type: cross Abstract: We propose an imitation-learning design for neural-network control policies under state and input constraints. Training alternates a standard imitation gradient step with a block of $k$ safety steps that pull the network's actions toward their projection onto the safe set; at run time, the controller is the trained network alone, with no safety filter. We analyze this scheme as inexact projected gradient descent in the space of policy actions. Wh