Provably Efficient Adversarial Imitation Learning with Unknown Transitions - Oral Session 4

Explore a 27-minute conference talk from the Uncertainty in Artificial Intelligence (UAI) 2023 Oral Session 4 that delves into the theoretical foundations of Adversarial Imitation Learning (AIL) in environments with unknown transitions. Learn about the challenges posed by stochastic and uncertain environment transitions in AIL and discover the innovative MB-TAIL algorithm, which achieves minimax optimal expert sample complexity and interaction complexity. Understand how this research connects reward-free exploration with AIL and extends to function approximation settings, advancing the field of imitation learning. Gain insights from the presented slides and the abstract of the paper "Provably Efficient Adversarial Imitation Learning with Unknown Transitions" by Tian Xu, Ziniu Li, Yang Yu, and Zhi-Quan Luo.