Offline Imitation Learning

Offline imitation learning (OIL) aims to train agents to mimic expert behavior using only pre-recorded data, without further environmental interaction. Current research focuses on addressing challenges like covariate shift (differences between expert and agent data distributions) and leveraging suboptimal demonstrations alongside expert data, employing techniques such as behavior cloning, inverse reinforcement learning, and various model-based and model-free approaches including transformer networks and diffusion models. These advancements are significant because they enable efficient skill acquisition in robotics and other domains where extensive real-world interaction is costly or impractical, leading to improved performance in tasks ranging from robotic manipulation to dialogue systems.