BanditRLlib
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Lean module · OFUL

BanditRLProof.OFULScheduledPowerOfTwoForcedHighProbabilityAverageConsistency

# Fixed-confidence average consistency for power-of-two forced OFUL 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.

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BanditRLProof.OFULScheduledPowerOfTwoForcedHighProbabilityAverageRegret

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BanditRLProof

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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.

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.

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.

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.

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