Cracking Open the Black Box - What Observations Can Tell Us About Reinforcement Learning Agents
NetAI@SIGCOMM, pp. 29-36, 2019.
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
Full Text (Upload PDF)
PPT (Upload PPT)