Adversarial Imitation Learning from State-only Demonstrations

By Faraz Torabi, Garrett Warnell, Peter Stone. 2019

30 citations recorded by the source corpus.

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 a new IfO approach based on generative adversarial networks called generative adversarial imitation from observation (GAIfO). 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.

Citations in the corpus

• Recent Advances in Imitation Learning from Observation (2019, background): “In another follow up work [Torabi et al. , 2019a], the authors modified the algo-rithm to make it more sample efficient in order to be able to execute it directly on physical robots.”