Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.

Aprominent challenge is to train an agent capable of generalising to new environments attest time, rather than one that simply memorises trajectories and visual details observed during training. We propose a new learning strategy that learns both from observations and generatedcounterfactual environments.