Data Efficient Third-person Imitation Learning Method

Imitation learning provides a framework to make agent learn an efficient policy from expert demonstrations.During the learning process,the agent does not need to interact with the expert or get access to an explicit reward signal,but only needs a large number of expert demonstrations.Classical imitation learning methods usually need to imitate from first-person expert demonstrations,a sequence of states and actions that expert should have taken.However,most expert demonstrations exist in the form of third-person videos in reality.Different from the first-person expert demonstrations,there is a difference between the viewpoint of the third-person demonstrations and samples generated by the agent,resulting in a lack of one-to-one correspondence between them.Therefore,the third-person demonstrations cannot be directly used in imitation learning.To alleviate this problem,this paper presents a data efficient third-person imitation learning method.Firstly,this method introduces the image difference based on Generat