TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubes
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914070438871040 |
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| author | Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie A. |
| author_facet | Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie A. |
| contents | Interactive Imitation Learning deals with training a novice policy from expert demonstrations in an online fashion. The established DAgger algorithm trains a robust novice policy by alternating between interacting with the environment and retraining of the network. Many variants thereof exist, that differ in the method of discerning whether to allow the novice to act or return control to the expert. We propose the use of stochastic reachtubes - common in verification of dynamical systems - as a novel method for estimating the necessity of expert intervention. Our approach does not require fine-tuning of decision thresholds per environment and effectively reduces the number of expert interventions, especially when compared with related approaches that make use of a doubt classification model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubes Lemmel, Julian Kranzl, Manuel Lamine, Adam Neubauer, Philipp Grosu, Radu Neubauer, Sophie A. Systems and Control Artificial Intelligence Machine Learning Interactive Imitation Learning deals with training a novice policy from expert demonstrations in an online fashion. The established DAgger algorithm trains a robust novice policy by alternating between interacting with the environment and retraining of the network. Many variants thereof exist, that differ in the method of discerning whether to allow the novice to act or return control to the expert. We propose the use of stochastic reachtubes - common in verification of dynamical systems - as a novel method for estimating the necessity of expert intervention. Our approach does not require fine-tuning of decision thresholds per environment and effectively reduces the number of expert interventions, especially when compared with related approaches that make use of a doubt classification model. |
| title | TubeDAgger: Reducing the Number of Expert Interventions with Stochastic Reach-Tubes |
| topic | Systems and Control Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2510.00906 |