Learning.IT.obs
From the authors
obs n is the observation at time n. This is a random variable on the measurable space
โ โ Round ๐ ๐ ๐จ.
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๐ : Type u_1 -
๐ : Type u_2 -
๐จ : Type u_3
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n : โ -
h : โ โ Round ๐ ๐ ๐จOne round of interaction: an observation, then an action, then a feedback.
๐(h n).obsNat : TypeThe natural numbers, starting at zero. This type is special-cased by both the kernel and the compiler, and overridden with an efficient implementation. Both use a fast arbitrary-precision arithmetic library (usually [GMP](https://gmplib.org/)); at runtime, `Nat` values that are sufficiently small are unboxed.
Learning.Round : Type u_5 โ Type u_6 โ Type u_7 โ Type (max u_5 u_7 u_6)One round of interaction: an observation, then an action, then a feedback.Go to its page
Learning.Round.obs : {๐ : Type u_1} โ {๐ : Type u_2} โ {๐จ : Type u_3} โ Learning.Round ๐ ๐ ๐จ โ ๐The observation of a round.Go to its page
Code
def obs (n : โ) (h : โ โ Round ๐ ๐ ๐จ) : ๐ := (h n).obs
New in v4.34.0-rc2-74-ge05e4f3 (2026-09-10), and its meaning has not changed since.
Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file ยท open it in the Lean web editor.
Dependency graph
Audit surface: 2 project declarations, 3 external constants
โ Proved: no sorry anywhere in its closure
This is the tool's own reading of one build's recorded axioms, and it is not robust against an author who wants it to pass. Checking meant to be relied on should go through Comparator, which replays the proof through the kernel from an export against an explicit list of permitted axioms.