Behavior Cloning

Behavior cloning (BC) is a machine learning technique that trains agents to mimic expert behavior by learning from observational data, primarily aiming to replicate complex skills without requiring extensive trial-and-error learning. Current research focuses on improving BC's robustness to noisy or incomplete data, often employing techniques like weighted behavioral cloning, reinforcement learning fine-tuning, and diffusion models to enhance generalization and address issues like covariate shift and compounding errors. These advancements are significant for various applications, including robotics, autonomous driving, and other domains where learning from expert demonstrations is more efficient than traditional reinforcement learning approaches.