Robot Learning

Inverse Reinforcement Learning (IRL)

Inverse reinforcement learning is the problem of inferring the reward function an expert is optimizing from observations of its behavior, formalized by Ng and Russell in 2000. Maximum-entropy IRL (Ziebart et al., 2008) resolved the ambiguity among consistent rewards probabilistically, and adversarial descendants such as GAIL match the expert's state-action distribution without explicitly recovering a reward.

Why it matters for physical AI

Recovering the objective behind demonstrations yields policies that generalize beyond copied trajectories and transfers human preferences into reward signals usable for further reinforcement learning.