Xiaogang Yuan

SC-AIRL: Share-Critic in Adversarial Inverse Reinforcement Learning for Long-Horizon Task

Adversarial Inverse Reinforcement Learning (AIRL) has gained popularity as an alternative to supervised imitation learning, addressing the distributional bias issue of the latter. However, it still faces significant challenges in long-horizon tasks due to the lack of effective exploration. In our le