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

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title = "Safe Reinforcement Learning Using Black-Box Reachability Analysis",

keywords = "Reinforcement learning, robot safety, task and motion planning",

author = "Mahmoud Selim and Amr Alanwar and Shreyas Kousik and Grace Gao and Marco Pavone and Johansson, {Karl H.}",

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T1 - Safe Reinforcement Learning Using Black-Box Reachability Analysis

AU - Selim, Mahmoud

AU - Alanwar, Amr

AU - Kousik, Shreyas

AU - Gao, Grace

AU - Pavone, Marco

AU - Johansson, Karl H.

PY - 2022/10/1

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KW - Reinforcement learning

KW - robot safety

KW - task and motion planning

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M3 - Article

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JO - IEEE Robotics and Automation Letters

JF - IEEE Robotics and Automation Letters

IS - 4

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Safe Reinforcement Learning Using Black-Box Reachability Analysis

ASJC Scopus Subject Areas

Keywords

Access to Document

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