MOSS large-gap arm expected pull bound
DisprovedBanditAlgorithm.moss_large_gap_arm_expected_pull_bound⚠️ Retired — specification defect
The Lean statement below does not encode the problem shown on this page, so its
Disprovedstatus carries no information about that problem. Do not import this node or use it as a dependency.
Fix an arm with in a 1-subgaussian bandit and run MOSS for horizon . Then . The source defines , proves from the MOSS index rule, and applies Lemma 8.2 to obtain this displayed armwise bound.
Why this node was retired
The posted statement is
theorem BanditAlgorithm.moss_large_gap_arm_expected_pull_bound
{k : ℕ} (hk : 0 < k)
{ν : BanditAlgorithm.StochasticBandit k}
(hν : BanditAlgorithm.IsSubgaussianBandit 1 ν)
{n : ℕ} {π : BanditAlgorithm.BanditPolicy k}
(hπ : BanditAlgorithm.IsMOSSPolicy n π) (hkn : k ≤ n)
(i : Fin k)
(hi : 8 * Real.sqrt ((k : ℝ) / n) < BanditAlgorithm.banditGap ν i) :
BanditAlgorithm.banditGap ν i *
MeasureTheory.integral
(BanditAlgorithm.banditMeasure ν π n)
(fun h ↦ (BanditAlgorithm.armPullCount i h : ℝ)) ≤
BanditAlgorithm.banditGap ν i + 15 * Real.sqrt ((n : ℝ) / k) := by
sorry
The source only compares pulls to κ under a random optimal-index shortfall cutoff; the target asserts an unconditional per-arm pull bound.
The recorded counterexample refutes the statement as encoded. It says nothing about the problem shown above, which is a different proposition.
Proposed corrected statement
Retain the event Δᵢ>2Z, where Z is the optimal-arm index shortfall, in the pull-count comparison/regret decomposition; state the unconditional source estimate for ΔᵢE[κᵢ], not for ΔᵢE[Tᵢ(n)]. Use full potential streams in defining κ and Z.
Diagnosis and correction from the public Prove2Me statement audit (wamlat/prove2me-errors). The correction is natural-language mathematics and is not Lean-verified — it is a specification for a corrected node, not a drop-in replacement. No corrected replacement node exists yet.
import Definitions.Def_banditRegret import Definitions.Def_mossPolicy open MeasureTheory ProbabilityTheory
theorem BanditAlgorithm.moss_large_gap_arm_expected_pull_bound
{k : ℕ} (hk : 0 < k)
{ν : BanditAlgorithm.StochasticBandit k}
(hν : BanditAlgorithm.IsSubgaussianBandit 1 ν)
{n : ℕ} {π : BanditAlgorithm.BanditPolicy k}
(hπ : BanditAlgorithm.IsMOSSPolicy n π) (hkn : k ≤ n)
(i : Fin k)
(hi : 8 * Real.sqrt ((k : ℝ) / n) < BanditAlgorithm.banditGap ν i) :
BanditAlgorithm.banditGap ν i *
MeasureTheory.integral
(BanditAlgorithm.banditMeasure ν π n)
(fun h ↦ (BanditAlgorithm.armPullCount i h : ℝ)) ≤
BanditAlgorithm.banditGap ν i + 15 * Real.sqrt ((n : ℝ) / k) := by
sorry