This library provides research-quality implementations of imitation learning algorithms including Behavioral Cloning, DAgger, GAIL, and AIRL. It enables training policy models using expert demonstration data instead of hand-engineered reward functions. Built on PyTorch and integrated with Gymnasium, it offers both discrete and continuous action space support along with reward learning capabilities for inverse reinforcement learning tasks.