LeanMachineLearning

Bandits.UCB.expectation_pullCount_le'🔗

Lemma

From the authors

Bound on the expectation of the number of pulls of each arm by the UCB algorithm.

Types
  • Ω : Type u_1mΩ : MeasurableSpace ΩA measurable space is a space equipped with a σ-algebra.
Given
  • K : ℕNeZero K
  • c : ℝ
  • ν : ProbabilityTheory.Kernel (Fin K) ℝA kernel from a measurable space α to another measurable space β is a measurable function κ : α → Measure β.ProbabilityTheory.IsMarkovKernel νA kernel is a Markov kernel if every measure in its image is a probability measure.
  • P : MeasureTheory.Measure ΩA measure is defined to be an outer measure that is countably additive on measurable sets, with the additional assumption that the outer measure is the canonical extension of the restricted measure.MeasureTheory.IsProbabilityMeasure PA measure μ is called a probability measure if μ univ = 1.
  • O : ℕ → Ω → Unit
  • A : ℕ → Ω → Fin K
  • R : ℕ → Ω → ℝ
  • σ2 : NNReal
  • a : Fin K
  • n : ℕ
Assuming
Then
∫⁻ (ω : Ω), ↑(Learning.pullCount A a n ω) ∂P ≤
  ENNReal.ofReal (8 * c * ↑σ2 * Real.log (↑n + 1) / gap ν a ^ 2 + 1) + 1 + 2 * ENNReal.ofReal (constSum c n)
Code
lemma expectation_pullCount_le'
    (h : IsAlgEnvSeq O A R (ucbAlgorithm K (c * σ2)) (stationaryEnv ν) P)
    (hν : ∀ a, HasSubgaussianMGF (fun x ↦ x - (ν a)[id]) σ2 (ν a))
    (hσ2 : σ2 ≠ 0) (hc : 0 < c) (a : Fin K) (h_gap : 0 < gap ν a) (n : ℕ) :
    ∫⁻ ω, pullCount A a n ω ∂P ≤
      ENNReal.ofReal (8 * c * σ2 * log (n + 1) / gap ν a ^ 2 + 1) + 1 +
        2 * ENNReal.ofReal (constSum c n)
Proof
by
  have hA := h.measurable_action
  have hR := h.measurable_feedback
  by_cases hn_zero : n = 0
  · simp [hn_zero]
  let C a : ℕ := ⌈8 * c * σ2 * log (n + 1) / gap ν a ^ 2⌉₊
  have h_set_1 b : MeasurableSet {ω | 0 < pullCount A a b ω ∧
      (ν a)[id] < empMean A R a b ω - ucbWidth A (c * σ2) a b ω} := by
    simp only [measurableSet_setOfPred]
    fun_prop
  have h_set_2 b : MeasurableSet {ω | 0 < pullCount A (bestArm ν) b ω ∧
      empMean A R (bestArm ν) b ω + ucbWidth A (c * σ2) (bestArm ν) b ω < (ν (bestArm ν))[id]} := by
    simp only [measurableSet_setOfPred]
    fun_prop
  have h_meas_1 b : Measurable fun ω ↦ {s | 0 < pullCount A a s ω ∧ (ν a)[id] <
      empMean A R a s ω - ucbWidth A (c * σ2) a s ω}.indicator (1 : ℕ → ℕ) b := by
    simp only [id_eq, Set.indicator_apply, Set.mem_ofPred_eq, Pi.one_apply]
    exact Measurable.ite (h_set_1 _) (by fun_prop) (by fun_prop)
  have h_meas_2 b : Measurable fun ω ↦ {s | 0 < pullCount A (bestArm ν) s ω ∧
      empMean A R (bestArm ν) s ω + ucbWidth A (c * σ2) (bestArm ν) s ω <
          (ν (bestArm ν))[id]}.indicator (1 : ℕ → ℕ) b := by
    simp only [id_eq, Set.indicator_apply, Set.mem_ofPred_eq, Pi.one_apply]
    exact Measurable.ite (h_set_2 _) (by fun_prop) (by fun_prop)
  calc ∫⁻ ω, pullCount A a n ω ∂P
  _ ≤ ∫⁻ ω, C a + 1 +
      ∑ s ∈ range n,
        {s | 0 < pullCount A (bestArm ν) s ω ∧
          empMean A R (bestArm ν) s ω + ucbWidth A (c * σ2) (bestArm ν) s ω <
            (ν (bestArm ν))[id]}.indicator (1 : ℕ → ℕ) s +
      ∑ s ∈ range n,
        {s | 0 < pullCount A a s ω ∧ (ν a)[id] <
          empMean A R a s ω - ucbWidth A (c * σ2) a s ω}.indicator (1 : ℕ → ℕ) s ∂P := by
    refine lintegral_mono_ae ?_
    have hCa : C a ≠ 0 := by
      simp only [ne_eq, Nat.ceil_eq_zero, not_le, C]
      have : 0 < log (n + 1) := log_pos (by simp; grind)
      positivity
    filter_upwards [pullCount_ae_le_add_two h hc.le a h_gap n (C a) hCa (Nat.le_ceil _)] with ω hω
    simp only [id_eq, Nat.cast_sum]
    norm_cast
  _ ≤ (C a : ℝ≥0∞) + 1 +
      ∑ s ∈ range n,
        P {ω | 0 < pullCount A (bestArm ν) s ω ∧
          empMean A R (bestArm ν) s ω + ucbWidth A (c * σ2) (bestArm ν) s ω < (ν (bestArm ν))[id]} +
      ∑ s ∈ range n,
        P {ω | 0 < pullCount A a s ω ∧ (ν a)[id] <
          empMean A R a s ω - ucbWidth A (c * σ2) a s ω} := by
    simp only [id_eq, Nat.cast_sum]
    rw [lintegral_add_left (by fun_prop), lintegral_add_left (by fun_prop)]
    simp only [lintegral_const, measure_univ, mul_one]
    rw [lintegral_finsetSum _ (by fun_prop), lintegral_finsetSum _ (by fun_prop)]
    gcongr with k hk k hk
    · rw [← lintegral_indicator_one]
      swap; · exact h_set_2 _
      gcongr with ω
      simp [Set.indicator_apply]
    · rw [← lintegral_indicator_one]
      swap; · exact h_set_1 _
      gcongr with ω
      simp [Set.indicator_apply]
  _ ≤ (C a : ℝ≥0∞) + 1 +
      ∑ s ∈ range n, 1 / ((s : ℝ≥0∞) + 1) ^ (c - 1) +
      ∑ s ∈ range n, 1 / ((s : ℝ≥0∞) + 1) ^ (c - 1) := by
    gcongr with s hs s hs
    · refine (measure_mono ?_).trans (prob_ucbIndex_le h hν hσ2 (by positivity) (bestArm ν) s)
      grind
    · refine (measure_mono ?_).trans (prob_lcbIndex_ge h hν hσ2 (by positivity) a s)
      grind
  _ ≤ ENNReal.ofReal (8 * c * σ2 * log (n + 1) / gap ν a ^ 2 + 1) + 1 +
      2 * ENNReal.ofReal (constSum c n) := by
    rw [two_mul, add_assoc, ofReal_constSum]
    gcongr
    simp only [C]
    rw [← ENNReal.ofReal_natCast]
    refine ENNReal.ofReal_le_ofReal ?_
    refine (Nat.ceil_lt_add_one ?_).le
    have : 0 ≤ log (n + 1) := log_nonneg (by simp)
    positivity

Meaning last changed in v4.34.0-rc2-90-gdde3322 (2026-09-16), the 9th recorded change.

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

Dependency graph

Audit surface: 24 project declarations, 143 external constants

✓ 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.