観察からの模倣学習の最近の進展
Recent Advances in Imitation Learning from Observation
専門家の状態情報（動画など）のみから模倣する観察からの模倣学習（IfO）の研究をレビューし、未解決課題と今後の方向性を示した論文。
著者: Faraz Torabi, Garrett Warnell, Peter Stone
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Imitation learning is the process by which one agent tries to learn how to perform a certain task using information generated by another, often more-expert agent performing that same task. Conventionally, the imitator has access to both state and action information generated by an expert performing the task (e.g., the expert may provide a kinesthetic demonstration of object placement using a robotic arm). However, requiring the action information prevents imitation learning from a large number of existing valuable learning resources such as online videos of humans performing tasks. To overcome this issue, the specific problem of imitation from observation (IfO) has recently garnered a great deal of attention, in which the imitator only has access to the state information (e.g., video frames) generated by the expert. In this paper, we provide a literature review of methods developed for IfO, and then point out some open research problems and potential future work.
