Ranking-Based Generative Adversarial Imitation Learning

In imitation learning, it is often assumed the demonstration data are optimal, even though they are imperfect in practice. The imperfect demonstrations result from expert errors, large-scale demonstration data, and the non-convexity of the solution space of the task. In this paper, we propose a new ranking-based Generative Adversarial Imitation Learning (RB-GAIL) that can deal with the above imperfect datasets by utilizing the generated experiences more efficiently and avoiding the dependency on plenty of different expert demonstrations. We performed a rigorous mathematical analysis, indicating that RB-GAIL can implicitly model the modes of the expert data by weighting multiple discriminators, and a monotonically increasing positive activation function can help the model converge to the global optimal solution. Experimental results show that our method surpasses other baseline methods with imperfect demonstration (ours: increased by 4.7% to the optimal expert level in the Ant task, but Trajectory-ranked Reward Extrapolation (T-REX): decreased by 12.2%, Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning (UID): decreased by 31.01% and Wasserstein Adversarial Imitation Learning (WAIL): dropped by 96.0%). In physical experiments with manipulation, our proposed method achieved a success rate of 100% (WAIL: under 90%).