Kee-Eung Kim

DPAIL: Training Diffusion Policy for Adversarial Imitation Learning without Policy Optimization

DPAIL, an adversarial IL framework that leverages diffusion models as a policy class to enhance expressiveness, trains a diffusion policy using a binary cross-entropy objective to distinguish expert trajectories from generated ones to enable optimization of the diffusion policy.