Cracking Open the Black Box - What Observations Can Tell Us About Reinforcement Learning Agents

Arnaud Dethise
Arnaud Dethise
Marco Canini
Marco Canini

NetAI@SIGCOMM, pp. 29-36, 2019.

Cited by: 3|Bibtex|Views64|DOI:https://doi.org/10.1145/3341216.3342210
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Other Links: dblp.uni-trier.de|dl.acm.org|academic.microsoft.com

Abstract:

Machine learning (ML) solutions to challenging networking problems, while promising, are hard to interpret; the uncertainty about how they would behave in untested scenarios has hindered adoption. Using a case study of an ML-based video rate adaptation model, we show that carefully applying interpretability tools and systematically explor...More

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