Safe Reinforcement Learning Using Black-Box Reachability Analysis

Mahmoud Selim; Amr Alanwar; Shreyas Kousik; Grace Gao; Marco Pavone; Karl H. Johansson

doi:10.1109/LRA.2022.3192205

Safe Reinforcement Learning Using Black-Box Reachability Analysis

Mahmoud Selim, Amr Alanwar, Shreyas Kousik, Grace Gao, Marco Pavone, Karl H. Johansson

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	10665-10672
Number of pages	8
Journal	IEEE Robotics and Automation Letters
Volume	7
Issue number	4
DOIs	https://doi.org/10.1109/LRA.2022.3192205
Publication status	Published - Oct 1 2022
Externally published	Yes

ASJC Scopus Subject Areas

Control and Systems Engineering
Biomedical Engineering
Human-Computer Interaction
Mechanical Engineering
Computer Vision and Pattern Recognition
Computer Science Applications
Control and Optimization
Artificial Intelligence

Keywords

Reinforcement learning
robot safety
task and motion planning

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10.1109/LRA.2022.3192205

Cite this

Selim, M., Alanwar, A., Kousik, S., Gao, G., Pavone, M., & Johansson, K. H. (2022). Safe Reinforcement Learning Using Black-Box Reachability Analysis. IEEE Robotics and Automation Letters, 7(4), 10665-10672. https://doi.org/10.1109/LRA.2022.3192205

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AU - Johansson, Karl H.

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Safe Reinforcement Learning Using Black-Box Reachability Analysis

ASJC Scopus Subject Areas

Keywords

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