Towards Verification and Validation of Reinforcement Learning in Safety-Critical Systems: A Position Paper from the Aerospace Industry

Nikko, ErikLinköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,Saab Aeronautics,ReaL / AILAB (författare)

Towards Verification and Validation of Reinforcement Learning in Safety-Critical Systems : A Position Paper from the Aerospace Industry

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.

Sjanic, Zoran,1975-Linköpings universitet,Reglerteknik,Tekniska fakulteten,Saab Aeronautics(Swepub:liu)zorsj26 (författare)
Heintz, Fredrik,1975-Linköpings universitet,Artificiell intelligens och integrerade datorsystem,Tekniska fakulteten,ReaL / AILAB(Swepub:liu)frehe08 (författare)
Linköpings universitetArtificiell intelligens och integrerade datorsystem (creator_code:org_t)

Ingår i:Robust and Reliable Autonomy in the Wild, International Joint Conferences on Artificial Intelligence

Av författaren/redakt...: Nikko, Erik; Sjanic, Zoran, 1 ...; Heintz, Fredrik, ...

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