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Towards Verification and Validation of Reinforcement Learning in Safety-Critical Systems : A Position Paper from the Aerospace Industry
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- Nikko, Erik (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,Saab Aeronautics,ReaL / AILAB
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- Sjanic, Zoran, 1975- (author)
- Linköpings universitet,Reglerteknik,Tekniska fakulteten,Saab Aeronautics
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- Heintz, Fredrik, 1975- (author)
- Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,ReaL / AILAB
- (creator_code:org_t)
- 2021
- 2021
- English.
- In: Robust and Reliable Autonomy in the Wild, International Joint Conferences on Artificial Intelligence.
- Conference paper (other academic/artistic)
Abstract
Subject headings
Close
- Reinforcement learning techniques have successfully been applied to solve challenging problems. Among the more famous examples are playing games such as Go and real-time computer games such as StarCraft II. In addition, reinforcement learning has successfully been deployed in cyber-physical systems such as robots playing a curling-based game. These are all important and significant achievements indicating that the techniques can be of value for the aerospace industry. However, to use these techniques in the aerospace industry, very high requirements on verification and validation must be met. In this position paper, we outline four key problems for verification and validation of reinforcement learning techniques. Solving these are an important step towards enabling reinforcement learning techniques to be used in safety critical domains such as the aerospace industry.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Safe Reinforcement Learning
- Verification and Validation
- Safety-critical systems
- Cyber-physical systems
- Reinforcement Learning
- Aerospace
Publication and Content Type
- vet (subject category)
- kon (subject category)
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