Lean module · OFUL
BanditRLProof.OFULScheduledPowerOfTwoForcedHighProbabilityAverageConsistency
This module squeezes complete pseudo-regret per round to zero on every trajectory outside the existing all-horizon violation event. It then bounds the set of trajectories where this limit fails by the same fixed outer confidence budget, under the same policy and canonical measure.
Module map
Imports
BanditRLProof.OFULScheduledPowerOfTwoForcedHighProbabilityAverageRegret
Imported by
Declarations
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theorem
BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_of_not_mem_allHorizonViolationSet
Compiled
Complete fixed-best average pseudo-regret tends to zero on every trajectory outside the fixed-model power-of-two forced all-horizon violation event.
Used in these reading views: Bandit Book
5. OFUL, self-normalized confidence, and stopping times
Canonical node identity
declaration:BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_of_not_mem_allHorizonViolationSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_of_not_mem_allHorizonViolationSet {K : Nat} {Feature : Type u} [Fintype Feature] [Nonempty Feature] (lambda : Real) (hlambda : 0 < lambda) (thetaStar : Feature -> Real) (actionFeature : Fin K -> Feature -> Real) (R : Real) (delta : Real) (hdelta : 0 < delta) (S L2 : Real) (hL2 : 0 <= L2) (best : Fin K) (hbest : IsOptimalLinearArm thetaStar actionFeature best) (trajectory : Nat -> Fin K × Real) (hnot : trajectory ∉ powerOfTwoForcedCanonicalAsymptoticHighProbabilityPseudoRegretAllHorizonViolationSet lambda thetaStar actionFeature R delta S L2 best) : Tendsto (fun horizon => powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret thetaStar actionFeature best horizon trajectory) atTop (nhds 0)
def
BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet
Compiled
The trajectories where complete fixed-best average pseudo-regret does not converge to zero.
Used in these reading views: Bandit Book
5. OFUL, self-normalized confidence, and stopping times
Canonical node identity
declaration:BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet {K : Nat} {Feature : Type u} [Fintype Feature] (thetaStar : Feature -> Real) (actionFeature : Fin K -> Feature -> Real) (best : Fin K) : Set (Nat -> Fin K × Real)
theorem
BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet_subset_allHorizonViolationSet
Compiled
Failure of trajectory-level average consistency can only occur inside the existing all-horizon violation event.
Used in these reading views: Bandit Book
5. OFUL, self-normalized confidence, and stopping times
Canonical node identity
declaration:BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet_subset_allHorizonViolationSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet_subset_allHorizonViolationSet {K : Nat} {Feature : Type u} [Fintype Feature] [Nonempty Feature] (lambda : Real) (hlambda : 0 < lambda) (thetaStar : Feature -> Real) (actionFeature : Fin K -> Feature -> Real) (R : Real) (delta : Real) (hdelta : 0 < delta) (S L2 : Real) (hL2 : 0 <= L2) (best : Fin K) (hbest : IsOptimalLinearArm thetaStar actionFeature best) : powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet thetaStar actionFeature best ⊆ powerOfTwoForcedCanonicalAsymptoticHighProbabilityPseudoRegretAllHorizonViolationSet lambda thetaStar actionFeature R delta S L2 best
theorem
BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_off_violation_and_consistencyFailure_tail_le_of_linearSubgaussianEnvironment_of_featureBound_le_regularization
Compiled
Complete fixed-confidence trajectory-level average-consistency theorem for one power-of-two forced policy. Outside the unchanged all-horizon event average pseudo-regret tends to zero, and the set of trajectories where this convergence fails has outer measure at most `delta` under the same canonical measure.
Used in these reading views: Bandit Book
5. OFUL, self-normalized confidence, and stopping times
Canonical node identity
declaration:BanditRLProof.OFUL.powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_off_violation_and_consistencyFailure_tail_le_of_linearSubgaussianEnvironment_of_featureBound_le_regularizationReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret_tendsto_zero_off_violation_and_consistencyFailure_tail_le_of_linearSubgaussianEnvironment_of_featureBound_le_regularization {K : Nat} {Feature : Type u} [Fintype Feature] [DecidableEq Feature] [Nonempty Feature] (hK : 0 < K) (lambda : Real) (hlambda : 0 < lambda) (thetaStar : Feature -> Real) (actionFeature : Fin K -> Feature -> Real) (R : Real) (hR : 0 < R) (delta : Real) (hdelta : 0 < delta) (hdelta_one : delta <= 1) (S : Real) (hS : 0 <= S) (forcedAction : Nat -> Fin K) (environment : Thompson.HistoryEnvironment (Fin K) Real) (L2 : Real) (hL2 : 0 <= L2) (hactionFeatureBound : forall action, dotProduct (actionFeature action) (actionFeature action) <= L2) (hL2lambda : L2 <= lambda) (best : Fin K) (hbest : IsOptimalLinearArm thetaStar actionFeature best) (source : CanonicalLinearSubgaussianEnvironmentLaw hK thetaStar actionFeature R S environment) : (forall trajectory, trajectory ∉ powerOfTwoForcedCanonicalAsymptoticHighProbabilityPseudoRegretAllHorizonViolationSet lambda thetaStar actionFeature R delta S L2 best -> Tendsto (fun horizon => powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegret thetaStar actionFeature best horizon trajectory) atTop (nhds 0)) ∧ Thompson.canonicalHistoryTrajectoryMeasure (finiteHistoryPowerOfTwoForcedTelescopingScalarRidgeAlgorithm hK lambda actionFeature R delta S forcedAction) environment (powerOfTwoForcedCanonicalStandardHighProbabilityAveragePseudoRegretConsistencyFailureSet thetaStar actionFeature best) <= ENNReal.ofReal delta