The Robotics Institute

Carnegie Mellon Robotics Institute

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Abstract

Sequential prediction problems such as imitation learning, where future observations depend on previous predictions (actions), violate the common i.i.d. assumptions made in statistical learning. This leads to poor performance in theory and often in practice. Some recent approaches provide stronger guarantees in this setting, but remain somewhat unsatisfactory as they train either non-stationary or stochastic policies and require a large number of iterations. In this paper, we propose a new iterative algorithm, which trains a stationary deterministic policy, that can be seen as a no regret algorithm in an online learning setting. We show that any such no regret algorithm, combined with additional reduction assumptions, must find a policy with good performance under the distribution of observations it induces in such sequential settings. We demonstrate that this new approach outperforms previous approaches on two challenging imitation learning problems and a benchmark sequence labeling problem.

Keywords

Imitation Learning, Structured Prediction, Reduction, Online Learning

Notes

Sponsor: ONR MURI grant N00014-09-1-1052 Reasoning in Reduced Information Spaces, National Sciences and Engineering Research Council of Canada (NSERC)

Associated Center(s) / Consortia: National Robotics Engineering Center

Number of pages: 9

Text Reference

Stephane Ross, Geoffrey Gordon, and J. Andrew (Drew) Bagnell, "A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning," Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTATS), April, 2011.

BibTeX Reference

@inproceedings{Ross_2011_6814,
   author = "Stephane Ross and Geoffrey Gordon and J. Andrew (Drew) Bagnell",
   title = "A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning",
   booktitle = "Proceedings of the 14th International Conference on Artifical Intelligence and Statistics (AISTATS)",
   month = "April",
   year = "2011",
}

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