LeanMachineLearning

Learning.bayesStationaryEnvπŸ”—

Definition

An environment with observations in 𝓔 Γ— 𝓨. The first element e of an observation is sampled from Q once and remains constant. The second element of an observation is sampled from ΞΊ (e, a), where a is the corresponding action.

πŸ”—def
Learning.bayesStationaryEnv.{u_1, u_2, u_3} {𝓔 : Type u_1}
  {𝓐 : Type u_2} {𝓨 : Type u_3} [MeasurableSpace 𝓔] [MeasurableSpace 𝓐]
  [MeasurableSpace 𝓨] (Q : MeasureTheory.Measure 𝓔)
  [MeasureTheory.IsProbabilityMeasure Q]
  (ΞΊ : ProbabilityTheory.Kernel (𝓔 Γ— 𝓐) 𝓨)
  [ProbabilityTheory.IsMarkovKernel ΞΊ] : Environment 𝓐 (𝓔 Γ— 𝓨)
Learning.bayesStationaryEnv.{u_1, u_2,
    u_3}
  {𝓔 : Type u_1} {𝓐 : Type u_2}
  {𝓨 : Type u_3} [MeasurableSpace 𝓔]
  [MeasurableSpace 𝓐] [MeasurableSpace 𝓨]
  (Q : MeasureTheory.Measure 𝓔)
  [MeasureTheory.IsProbabilityMeasure Q]
  (ΞΊ : ProbabilityTheory.Kernel (𝓔 Γ— 𝓐) 𝓨)
  [ProbabilityTheory.IsMarkovKernel ΞΊ] :
  Environment 𝓐 (𝓔 Γ— 𝓨)

Code

noncomputable def bayesStationaryEnv (Q : Measure 𝓔) [IsProbabilityMeasure Q] (ΞΊ : Kernel (𝓔 Γ— 𝓐) 𝓨) [IsMarkovKernel ΞΊ] : Environment 𝓐 (𝓔 Γ— 𝓨) where feedback n := let g : (Iic n β†’ 𝓐 Γ— 𝓔 Γ— 𝓨) Γ— 𝓐 β†’ 𝓔 Γ— 𝓐 := fun (h, a) => ((h ⟨0, by simp⟩).2.1, a) (Kernel.deterministic (Prod.fst ∘ g) (by fun_prop)) Γ—β‚– (ΞΊ.comap g (by fun_prop)) Ξ½0 := (Kernel.const _ Q) βŠ—β‚– ΞΊ.swapLeft

Actions: Source Β· Open Issue

Meaning last changed in v4.34.0-rc2-1-g439785b (2026-08-23), the 4th recorded change.

Self-contained, with its dependencies inlined and proofs replaced by sorry: download the raw file Β· open it in the Lean web editor.

Dependency graph

Audit surface: 1 project declarations, 33 external constants

βœ“ 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.