Robot Learning

GAIL (Generative Adversarial Imitation Learning)

GAIL is an imitation learning algorithm, introduced by Ho and Ermon (2016), that trains a policy adversarially against a discriminator which tries to distinguish the policy's state-action occupancy from that of expert demonstrations. The discriminator's output serves as a reward signal for reinforcement learning, allowing the agent to match expert behavior without explicitly recovering a reward function and while gathering its own environment interactions.

Why it matters for physical AI

Adversarial imitation can outperform behavior cloning when demonstrations are scarce, since the learner explores beyond demonstrated states; the idea shaped how reward signals are extracted from human data for robot training.