Imitation Learning: Progress, Taxonomies and Challenges. (arXiv:2106.12177v2 [cs.LG] UPDATED)

Imitation learning aims to extract knowledge from human experts'
demonstrations or artificially created agents in order to replicate their
behaviors. Its success has been demonstrated in areas such as video games,
autonomous driving, robotic simulations and object manipulation. However, this
replicating process could be problematic, such as the performance is highly
dependent on the demonstration quality, and most trained agents are limited to
perform well in task-specific environments. In this survey, we provide a
systematic review on imitation learning. We …

arxiv challenges imitation learning progress taxonomies