Sheng-Fan Cao

Interleaved Projected Gradient Descent for Safe Imitation Learning

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 net...