Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective

Inverse Reinforcement Learning (IRL) -- the problem of learning reward functions from demonstrations of an \emph{expert policy} -- plays a critical role in developing intelligent systems. While widely used in applications, theoretical understandings of IRL present unique challenges and remain less developed compared with standard RL. For example, it remains open how to do IRL efficiently in standard \emph{offline} settings with pre-collected data, where states are obtained from a \emph{behavior policy} (which could be the expert policy itself), and actions …

applications challenges cs.ai cs.lg example expert functions intelligent intelligent systems perspective policy reinforcement reinforcement learning role standard stat.ml systems