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This year, generative artificial intelligence bypassed the metaverse and became the next large thing in tech
The Twelfth Intercontinental Conference on Mastering Representations(2024) Preview Preview summary A well known problem of offline reinforcement learning (RL) is The spammy articles problem of concealed confounding. There, unobserved variables may influence both equally the steps taken from the agent plus the results noticed in the info. Hidden confounding can compromise the validity of any causal conclusion drawn from the data and provides A serious impediment to effective offline RL. On this paper, we tackle the situation of concealed confounding in the nonidentifiable environment. We propose a definition of uncertainty as a consequence of confounding bias, termed delphic uncertainty, which uses variation above suitable entire world styles, and differentiate it from the well-known epistemic and aleatoric uncertainties.