Lean module · Finite-horizon RL
BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdUnboundedHittingAfterIntegrableFiniteStoppingTime
This module upgrades each fixed-index genuine hittingAfter first passage from almost-sure finiteness to first-moment integrability. The proof uses the compiled fourth-power burn-in checkpoints. A cubic block-width envelope is summable against both the infinite model-tail budget and the exponentially small return share. Once the deterministic checkpoint regret rate lies below the fixed positive threshold, delayed checkpoints are contained in the compiled violation events.
Module map
Imports
BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdUnboundedHittingAfterAEFiniteEventualImmediateStoppingAndInMeasureConsistency, BanditRLProof.RL.FiniteHorizonNaturalCausalRealizedBehaviorRegretHighProbabilityExplicitSchedule, BanditRLProof.RL.FiniteHorizonNaturalCausalGrowingWindowGridStoppingTimeL1AverageRealizedBehaviorRegretConsistency, BanditRLProof.OFULScheduledUnboundedStoppingTimeExpectedRegret
Imported by
BanditRLProof, BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdUnboundedHittingAfterSquareIntegrableFiniteStoppingTime
Declarations
Open an item to read its exact compact statement and source link. Detailed teaching notes are linked when registered.
def
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityQuarticBlockWeight
Compiled
Cubic envelope for the width of one consecutive fourth-power checkpoint block.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityQuarticBlockWeightReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
def explicitHighProbabilityQuarticBlockWeight (n : Nat) : Nat
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityRounds_succ_sub_le_quarticBlockWeight
Compiled
A consecutive fourth-power checkpoint gap is bounded by the cubic block weight.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityRounds_succ_sub_le_quarticBlockWeightReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem explicitHighProbabilityRounds_succ_sub_le_quarticBlockWeight (n : Nat) : explicitHighProbabilityRounds (n + 1) - explicitHighProbabilityRounds n <= explicitHighProbabilityQuarticBlockWeight n
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledLocalDelta_le_inv_pow_six
Compiled
Positive horizon makes every local confidence share no larger than a shifted inverse sixth power.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledLocalDelta_le_inv_pow_sixReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem selfConsistentScheduledLocalDelta_le_inv_pow_six (mdp : MDP State Action) (hhorizon : 0 < mdp.horizon) (t : Nat) : AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledLocalDelta mdp t <= 1 / (((t + 2 : Nat) : Real) ^ 6)
def
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedInverseCubePairEnvelope
Compiled
Shifted inverse-cube envelope used after the cubic block width cancels three powers.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedInverseCubePairEnvelopeReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def quarticBlockShiftedInverseCubePairEnvelope (p : Nat × Nat) : ENNReal
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockShiftedInverseCubePairEnvelope_ne_top
Compiled
The shifted inverse-cube envelope is summable over checkpoint/tail-offset pairs.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockShiftedInverseCubePairEnvelope_ne_topReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem tsum_quarticBlockShiftedInverseCubePairEnvelope_ne_top : Ne (∑' p : Nat × Nat, quarticBlockShiftedInverseCubePairEnvelope p) ∞
def
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedCoordinateModelFailureCharge
Compiled
One checkpoint block weight times one shifted coordinate model-failure charge.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedCoordinateModelFailureChargeReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def quarticBlockShiftedCoordinateModelFailureCharge (mdp : MDP State Action) (p : Nat × Nat) : ENNReal
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedCoordinateModelFailureCharge_le_pairEnvelope
Compiled
Positive horizon makes the actual weighted coordinate charge fit the inverse-cube pair envelope.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockShiftedCoordinateModelFailureCharge_le_pairEnvelopeReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem quarticBlockShiftedCoordinateModelFailureCharge_le_pairEnvelope (mdp : MDP State Action) (hhorizon : 0 < mdp.horizon) (p : Nat × Nat) : quarticBlockShiftedCoordinateModelFailureCharge mdp p <= quarticBlockShiftedInverseCubePairEnvelope p
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockShiftedCoordinateModelFailureCharge_ne_top
Compiled
The actual weighted shifted coordinate charges have finite total ENNReal mass.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockShiftedCoordinateModelFailureCharge_ne_topReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem tsum_quarticBlockShiftedCoordinateModelFailureCharge_ne_top (mdp : MDP State Action) (hhorizon : 0 < mdp.horizon) : Ne (∑' p : Nat × Nat, quarticBlockShiftedCoordinateModelFailureCharge mdp p) ∞
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockWeight_mul_tailModelFailureBudget_eq_tsum_shiftedCoordinateCharge
Compiled
A model tail after `n+1`, charged by one quartic block weight, is the shifted coordinate row.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.quarticBlockWeight_mul_tailModelFailureBudget_eq_tsum_shiftedCoordinateChargeReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem quarticBlockWeight_mul_tailModelFailureBudget_eq_tsum_shiftedCoordinateCharge (mdp : MDP State Action) (n : Nat) : (explicitHighProbabilityQuarticBlockWeight n : ENNReal) * selfConsistentScheduledCausalTailModelFailureBudget mdp (n + 1) = ∑' j : Nat, quarticBlockShiftedCoordinateModelFailureCharge mdp (n, j)
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_tailModelFailureBudget_ne_top
Compiled
The fourth-power block weights are summable against the exact infinite model-tail budgets.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_tailModelFailureBudget_ne_topReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem tsum_quarticBlockWeight_mul_tailModelFailureBudget_ne_top (mdp : MDP State Action) (hhorizon : 0 < mdp.horizon) : Ne (∑' n : Nat, (explicitHighProbabilityQuarticBlockWeight n : ENNReal) * selfConsistentScheduledCausalTailModelFailureBudget mdp (n + 1)) ∞
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.summable_quarticBlockWeight_mul_explicitHighProbabilityReturnDelta
Compiled
A cubic fourth-power block weight times the explicit exponential return share is summable.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.summable_quarticBlockWeight_mul_explicitHighProbabilityReturnDeltaReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem summable_quarticBlockWeight_mul_explicitHighProbabilityReturnDelta : Summable (fun n : Nat => ((explicitHighProbabilityQuarticBlockWeight n : Nat) : Real) * explicitHighProbabilityReturnDelta n)
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_explicitHighProbabilityReturnDelta_ne_top
Compiled
The exact exponential return shares have finite total mass after quartic block charging.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_explicitHighProbabilityReturnDelta_ne_topReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem tsum_quarticBlockWeight_mul_explicitHighProbabilityReturnDelta_ne_top : Ne (∑' n : Nat, (explicitHighProbabilityQuarticBlockWeight n : ENNReal) * ENNReal.ofReal (explicitHighProbabilityReturnDelta n)) ∞
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_explicitPolynomialPrefixTailModelReturnFailureBudget_ne_top
Compiled
The exact checkpoint violation budgets remain summable after paying every quartic block width.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.tsum_quarticBlockWeight_mul_explicitPolynomialPrefixTailModelReturnFailureBudget_ne_topReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem tsum_quarticBlockWeight_mul_explicitPolynomialPrefixTailModelReturnFailureBudget_ne_top (mdp : MDP State Action) (hhorizon : 0 < mdp.horizon) : Ne (∑' n : Nat, (explicitHighProbabilityQuarticBlockWeight n : ENNReal) * explicitPolynomialPrefixTailModelReturnFailureBudget mdp n) ∞
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityRounds_add_le_add_sum_Ico_quarticBlockWeight
Compiled
Cubic block weights dominate every finite telescope of the fourth-power checkpoint grid.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.explicitHighProbabilityRounds_add_le_add_sum_Ico_quarticBlockWeightReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem explicitHighProbabilityRounds_add_le_add_sum_Ico_quarticBlockWeight (start width : Nat) : explicitHighProbabilityRounds (start + width) <= explicitHighProbabilityRounds start + (Finset.Ico start (start + width)).sum explicitHighProbabilityQuarticBlockWeight
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.natCast_succ_le_checkpoint_add_tsum_quarticBlockWeight_of_delayed
Compiled
A finite natural time is paid for by an initial checkpoint plus all preceding delayed blocks.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.natCast_succ_le_checkpoint_add_tsum_quarticBlockWeight_of_delayedReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem natCast_succ_le_checkpoint_add_tsum_quarticBlockWeight_of_delayed (start time : Nat) : (time + 1 : ENNReal) <= (explicitHighProbabilityRounds start + 1 : ENNReal) + ∑' n : Nat, if start <= n ∧ explicitHighProbabilityRounds n < time then (explicitHighProbabilityQuarticBlockWeight n : ENNReal) else 0
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integrable_untopA_add_one_of_eventually_quarticCheckpointTail
Compiled
Eventually summable fourth-power checkpoint crossing tails imply a finite first moment.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integrable_untopA_add_one_of_eventually_quarticCheckpointTailReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem integrable_untopA_add_one_of_eventually_quarticCheckpointTail {Omega : Type*} [MeasurableSpace Omega] (mu : Measure Omega) [IsProbabilityMeasure mu] (tau : Omega -> WithTop Nat) (htau : Measurable tau) (budget : Nat -> ENNReal) (hbudget : Ne (∑' n : Nat, (explicitHighProbabilityQuarticBlockWeight n : ENNReal) * budget n) ∞) (htail : ∀ᶠ n : Nat in atTop, mu {omega | (explicitHighProbabilityRounds n : WithTop Nat) < tau omega} <= budget n) : Integrable (fun omega => (((tau omega).untopA + 1 : Nat) : Real)) mu
def
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet
Compiled
Checkpoint trajectories whose fixed-index uncapped first passage has not yet occurred.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (scheduleIndex checkpointIndex : Nat) : Set (HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t))
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurableSet_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet
Compiled
Every fixed delayed-checkpoint event is measurable.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurableSet_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem measurableSet_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (scheduleIndex checkpointIndex : Nat) : MeasurableSet (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor scheduleIndex checkpointIndex)
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_subset_explicitPolynomialPrefixAverageRealizedBehaviorRegretViolationSet
Compiled
Once a checkpoint lies beyond the fixed base and its deterministic rate is below the fixed threshold, every delayed first passage violates the compiled checkpoint regret bound.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_subset_explicitPolynomialPrefixAverageRealizedBehaviorRegretViolationSetReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_subset_explicitPolynomialPrefixAverageRealizedBehaviorRegretViolationSet (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) {scheduleIndex checkpointIndex : Nat} (hindex : scheduleIndex <= checkpointIndex) (hrate : explicitPolynomialPrefixAverageRealizedBehaviorRegretRate mdp varianceProxy baseVisitFloor checkpointIndex <= selfConsistentScheduledNaturalCausalInverseSqrtFirstPassageThreshold scheduleIndex) : selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor scheduleIndex checkpointIndex ⊆ explicitPolynomialPrefixAverageRealizedBehaviorRegretViolationSet mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor checkpointIndex
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eventually_selfConsistentScheduledCausalSource_trajectoryMeasure_inverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_le
Compiled
For each fixed threshold index, checkpoint crossing tails are eventually bounded by the explicit summable burn-in-tail/model-return budget.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eventually_selfConsistentScheduledCausalSource_trajectoryMeasure_inverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_leReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem eventually_selfConsistentScheduledCausalSource_trajectoryMeasure_inverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet_le (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] [StandardBorelSpace State] [StandardBorelSpace Action] (rewardSource : mdp.MeanCompatibleRewardKernel) (varianceProxy : NNReal) (hvarianceProxy : 0 < varianceProxy) (law : rewardSource.UniformSubgaussianRewardLaw varianceProxy) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (baseVisitFloor : Real) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) (scheduleIndex : Nat) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor ∀ᶠ checkpointIndex in atTop, source.trajectoryMeasure (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterDelayedCheckpointSet mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor scheduleIndex checkpointIndex) <= explicitPolynomialPrefixTailModelReturnFailureBudget mdp checkpointIndex
theorem
BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_integrableFiniteStoppingTime
Compiled
Each fixed-index genuine uncapped inverse-square-root first passage is finite almost surely and has an integrable random horizon.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_integrableFiniteStoppingTimeReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_integrableFiniteStoppingTime (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] [StandardBorelSpace State] [StandardBorelSpace Action] (rewardSource : mdp.MeanCompatibleRewardKernel) (varianceProxy : NNReal) (hvarianceProxy : 0 < varianceProxy) (law : rewardSource.UniformSubgaussianRewardLaw varianceProxy) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (baseVisitFloor : Real) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) (scheduleIndex : Nat) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor BanditRLProof.OFUL.IntegrableFiniteStoppingTime source.trajectoryMeasure (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor scheduleIndex)