  Compared to reinforcement learning, imitation learning (IL) is a powerful
paradigm for training agents to learn control policies efficiently from expert
demonstrations. However, in most cases, obtaining demonstration data is costly
and laborious, which poses a significant challenge in some scenarios. A
promising alternative is to train agent learning skills via imitation learning
without expert demonstrations, which, to some extent, would extremely expand
imitation learning areas. To achieve such expectation, in this paper, we
propose Hindsight Generative Adversarial Imitation Learning (HGAIL) algorithm,
with the aim of achieving imitation learning satisfying no need of
demonstrations. Combining hindsight idea with the generative adversarial
imitation learning (GAIL) framework, we realize implementing imitation learning
successfully in cases of expert demonstration data are not available.
Experiments show that the proposed method can train policies showing comparable
performance to current imitation learning methods. Further more, HGAIL
essentially endows curriculum learning mechanism which is critical for learning
policies.
