Robust Imitation Learning from Noisy Demonstrations
Abstract
Robust learning from noisy demonstrations is a practical but highly challenging problem in imitation learning. In this paper, we first theoretically show that robust imitation learning can be achieved by optimizing a classification risk with a symmetric loss. Based on this theoretical finding, we then propose a new imitation learning method that optimizes the classification risk by effectively combining pseudo-labeling with co-training. Unlike existing methods, our method does not require additional labels or strict assumptions about noise distributions. Experimental results on continuous-control benchmarks show that our method is more robust compared to state-of-the-art methods.
Cite
Text
Tangkaratt et al. "Robust Imitation Learning from Noisy Demonstrations." Artificial Intelligence and Statistics, 2021.Markdown
[Tangkaratt et al. "Robust Imitation Learning from Noisy Demonstrations." Artificial Intelligence and Statistics, 2021.](https://mlanthology.org/aistats/2021/tangkaratt2021aistats-robust/)BibTeX
@inproceedings{tangkaratt2021aistats-robust,
title = {{Robust Imitation Learning from Noisy Demonstrations}},
author = {Tangkaratt, Voot and Charoenphakdee, Nontawat and Sugiyama, Masashi},
booktitle = {Artificial Intelligence and Statistics},
year = {2021},
pages = {298-306},
volume = {130},
url = {https://mlanthology.org/aistats/2021/tangkaratt2021aistats-robust/}
}