Wasserstein Adversarial Imitation Learning

Understand

Imitation Learning describes the problem of recovering an expert policy from demonstrations.

• While inverse reinforcement learning approaches are known to be very sample-efficient in terms of expert demonstrations, they usually require problem-dependent reward functions or a (task-)specific reward-function regularization.
• In this paper, we show a natural connection between inverse reinforcement learning approaches and Optimal Transport, that enables more general reward functions with desirable properties (e.g., smoothness).
• Based on our observation, we propose a novel approach called Wasserstein Adversarial Imitation Learning.