  Imitation from observation (IfO) is the problem of learning directly from
state-only demonstrations without having access to the demonstrator's actions.
The lack of action information both distinguishes IfO from most of the
literature in imitation learning, and also sets it apart as a method that may
enable agents to learn from a large set of previously inapplicable resources
such as internet videos. In this paper, we propose both a general framework for
IfO approaches and also a new IfO approach based on generative adversarial
networks called generative adversarial imitation from observation (GAIfO). We
conduct experiments in two different settings: (1) when demonstrations consist
of low-dimensional, manually-defined state features, and (2) when
demonstrations consist of high-dimensional, raw visual data. We demonstrate
that our approach performs comparably to classical imitation learning
approaches (which have access to the demonstrator's actions) and significantly
outperforms existing imitation from observation methods in high-dimensional
simulation environments.
