Offline Imitation

Offline imitation learning aims to train agents to mimic expert behavior using only pre-recorded demonstrations, without further environment interaction. Current research focuses on addressing challenges like limited and potentially suboptimal data by employing techniques such as weighted behavioral cloning, model-based approaches (including reverse augmentation and world models), and optimal transport methods to align agent and expert trajectories. These advancements are significant because they enable learning complex behaviors from limited data, with applications ranging from robotics and autonomous systems to sports analytics and personalized medicine.