Zhipeng Shi

Ranking-Based Generative Adversarial Imitation Learning

Inimitation 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 letter, we propose a new