Imitation learning is an approach in which an agent learns how to execute a
task by trying to mimic how one or more teachers perform it. This learning
approach offers a compromise between the time it takes to learn a new task and
the effort needed to collect teacher samples for the agent. It achieves this by
balancing learning from the teacher, who has some information on how to perform
the task, and deviating from their examples when necessary, such as states not
present in the teacher samples. Consequently, the field of imitation learning
has received much attention from researchers in recent years, resulting in many
new methods and applications. However, with this increase in published work and
past surveys focusing mainly on methodology, a lack of standardisation became
more prominent in the field. This non-standardisation is evident in the use of
environments, which appear in no more than two works, and evaluation processes,
such as qualitative analysis, that have become rare in current literature. In
this survey, we systematically review current imitation learning literature and
present our findings by (i) classifying imitation learning techniques,
environments and metrics by introducing novel taxonomies; (ii) reflecting on
main problems from the literature; and (iii) presenting challenges and future
directions for researchers.

模仿学习的文献内容进行了系统回顾，通过引入新的分类法对模仿学习技术、环境和度量进行了分类，反思了文献中的主要问题，并提出了研究者面临的挑战和未来发展方向。