Yikang Gui

Inversely Learning Transferable Rewards via Abstracted States

Inverse reinforcement learning (IRL) has made significant progress in recovering reward functions from expert demonstrations. However, a key challenge remains: how to extract reward functions that generalize across related but distinct tasks. In this paper, we address this by focusing on transferabl