Variational imitation learning with diverse-quality demonstrations

Voot Tangkaratt*, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

17 Citations (Scopus)


Learning from demonstrations can be challenging when the quality of demonstrations is diverse, and even more so when the quality is unknown and there is no additional information to estimate the quality. We propose a new method for imitation learning in such scenarios. We show that simple quality-estimation approaches might fail due to compounding error, and fix this issue by jointly estimating both the quality and reward using a variational approach. Our method is easy to implement within reinforcement-learning frameworks and also achieves state-of-The-Art performance on continuous-control benchmarks. Our work enables scalable and data-efficient imitation learning under more realistic settings than before.

Original languageEnglish
Title of host publicationProceedings of the 37th International Conference on Machine Learning, ICML 2020
EditorsHal Daumé III, Aarti Singh
PublisherML Research Press
Number of pages11
ISBN (Electronic)9781713821120
Publication statusPublished - Jul 2020
Event37th International Conference on Machine Learning, ICML 2020 - Virtual, Online
Duration: 13 Jul 202018 Jul 2020

Publication series

NameProceedings of Machine Learning Research
ISSN (Print)2640-3498


Conference37th International Conference on Machine Learning, ICML 2020
Internet address

Scopus Subject Areas

  • Computational Theory and Mathematics
  • Human-Computer Interaction
  • Software


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