Learning temporal task specifications from demonstrations
- Author
- Mattijs Baert (UGent) , Sam Leroux (UGent) and Pieter Simoens (UGent)
- Organization
- Project
- Abstract
- As we progress towards real-world deployment, the critical need for interpretability in reinforcement learning algorithms grows more pivotal, ensuring the safety and reliability of intelligent agents. This paper tackles the challenge of acquiring task specifications in linear temporal logic through expert demonstrations, aiming to alleviate the burdensome task of specification engineering. The rich semantics of temporal logics serve as an interpretable framework for delineating intricate, multi-stage tasks. We propose a method which iteratively learns a task specification and a nominal policy solving this task. In each iteration, the task specification is refined to better distinguish expert trajectories from trajectories sampled from the nominal policy. With this process we obtain a concise and interpretable task specification. Unlike previous work, our method is capable of learning directly from trajectories in the original state space and does not require predefined atomic propositions. We showcase the effectiveness of our method on multiple tasks in both an office and a Minecraft-inspired environment.
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01J914A7MG4B2042HT11123XAH
- MLA
- Baert, Mattijs, et al. Learning Temporal Task Specifications from Demonstrations. Vol. 14847, Springer, 2024, pp. 81–98, doi:10.1007/978-3-031-70074-3_5.
- APA
- Baert, M., Leroux, S., & Simoens, P. (2024). Learning temporal task specifications from demonstrations. 14847, 81–98. https://doi.org/10.1007/978-3-031-70074-3_5
- Chicago author-date
- Baert, Mattijs, Sam Leroux, and Pieter Simoens. 2024. “Learning Temporal Task Specifications from Demonstrations.” In , 14847:81–98. Cham: Springer. https://doi.org/10.1007/978-3-031-70074-3_5.
- Chicago author-date (all authors)
- Baert, Mattijs, Sam Leroux, and Pieter Simoens. 2024. “Learning Temporal Task Specifications from Demonstrations.” In , 14847:81–98. Cham: Springer. doi:10.1007/978-3-031-70074-3_5.
- Vancouver
- 1.Baert M, Leroux S, Simoens P. Learning temporal task specifications from demonstrations. In Cham: Springer; 2024. p. 81–98.
- IEEE
- [1]M. Baert, S. Leroux, and P. Simoens, “Learning temporal task specifications from demonstrations,” presented at the 6th International Workshop, EXTRAAMAS 2024, Auckland, New Zealand, 2024, vol. 14847, pp. 81–98.
@inproceedings{01J914A7MG4B2042HT11123XAH,
abstract = {{As we progress towards real-world deployment, the critical need for interpretability in reinforcement learning algorithms grows more pivotal, ensuring the safety and reliability of intelligent agents. This paper tackles the challenge of acquiring task specifications in linear temporal logic through expert demonstrations, aiming to alleviate the burdensome task of specification engineering. The rich semantics of temporal logics serve as an interpretable framework for delineating intricate, multi-stage tasks. We propose a method which iteratively learns a task specification and a nominal policy solving this task. In each iteration, the task specification is refined to better distinguish expert trajectories from trajectories sampled from the nominal policy. With this process we obtain a concise and interpretable task specification. Unlike previous work, our method is capable of learning directly from trajectories in the original state space and does not require predefined atomic propositions. We showcase the effectiveness of our method on multiple tasks in both an office and a Minecraft-inspired environment.}},
author = {{Baert, Mattijs and Leroux, Sam and Simoens, Pieter}},
isbn = {{9783031700736}},
issn = {{0302-9743}},
language = {{eng}},
location = {{Auckland, New Zealand}},
pages = {{81--98}},
publisher = {{Springer}},
title = {{Learning temporal task specifications from demonstrations}},
url = {{http://doi.org/10.1007/978-3-031-70074-3_5}},
volume = {{14847}},
year = {{2024}},
}
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