BanditRLlib
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Lean module · Finite-horizon RL

BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegretAndReturnDeviationL1Consistency

This module evaluates the pathwise average successor-policy value gap and the normalized return deviation at the same genuine uncapped hittingAfter prefix as the accepted stopped realized-regret process. A deterministic 2H envelope and almost-everywhere random-prefix composition give behavior expected-regret L1 consistency. The exact realized/behavior/return decomposition then gives return-deviation L1 consistency.

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BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdCappedUnboundedHittingAfterExpectedRegretTruncationReplacement, BanditRLProof.RL.FiniteHorizonNaturalCausalBoundedStoppingTimeExplicitDeterministicMomentExpectedAverageRealizedBehaviorRegret

Imported by

BanditRLProof, BanditRLProof.RL.FiniteHorizonNaturalCausalInverseSqrtThresholdCappedUnboundedHittingAfterStoppedBehaviorExpectedRegretAndReturnDeviationL1TruncationEquivalence

Declarations

Open an item to read its exact compact statement and source link. Detailed teaching notes are linked when registered.

def BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess Compiled

Average successor-policy expected regret evaluated at a stopping prefix.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

noncomputable def selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> Real
Source line 35 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_apply Compiled

No declaration docstring is present; use the chapter context and exact statement below.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_apply

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_apply (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory = selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor (stoppingPrefix scheduleIndex trajectory).untopA trajectory
Source line 60 Chapter context: Finite-horizon RL
def BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess Compiled

Average normalized return deviation evaluated at a stopping prefix.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

noncomputable def selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> Real
Source line 88 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess_apply Compiled

No declaration docstring is present; use the chapter context and exact statement below.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess_apply

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess_apply (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory = selfConsistentScheduledNaturalCausalAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor (stoppingPrefix scheduleIndex trajectory).untopA trajectory
Source line 113 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess Compiled

Every deterministic-prefix average behavior expected-regret coordinate is measurable.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measurable_selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (rounds : Nat) : Measurable (selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor rounds)
Source line 142 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_nonneg Compiled

Every deterministic-prefix average behavior expected regret is nonnegative, including the zero-prefix convention.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_nonneg

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_nonneg (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (rounds : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : 0 <= selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor rounds trajectory
Source line 161 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_le_two_mul_horizon Compiled

The deterministic-prefix average behavior expected regret has the global policy-value envelope `2H`.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_le_two_mul_horizon

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess_le_two_mul_horizon (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (rounds : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : selfConsistentScheduledNaturalCausalAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor rounds trajectory <= 2 * (mdp.horizon : Real)
Source line 185 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess Compiled

A measurable stopping prefix gives a measurable stopped behavior expected-regret coordinate.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measurable_selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (hstopping : IsStoppingTime (selfConsistentScheduledNaturalCausalTrajectoryFiltration mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor) (stoppingPrefix scheduleIndex)) : Measurable (selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex)
Source line 217 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_nonneg Compiled

Stopping preserves nonnegativity of the pathwise behavior expected-regret average.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_nonneg

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_nonneg (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : 0 <= selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory
Source line 255 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_le_two_mul_horizon Compiled

Stopping preserves the deterministic `2H` policy-value envelope.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_le_two_mul_horizon

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess_le_two_mul_horizon (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory <= 2 * (mdp.horizon : Real)
Source line 283 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageRealizedBehaviorRegretProcess_eq_behaviorExpected_sub_returnDeviation Compiled

The stopped realized process is exactly stopped behavior expected regret minus stopped normalized return deviation at the same prefix.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalStoppingTimeAverageRealizedBehaviorRegretProcess_eq_behaviorExpected_sub_returnDeviation

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalStoppingTimeAverageRealizedBehaviorRegretProcess_eq_behaviorExpected_sub_returnDeviation (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (stoppingPrefix : Nat -> HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t) -> WithTop Nat) (scheduleIndex : Nat) (trajectory : HeterogeneousStochasticEpisodeBatchTrajectory mdp (fun t => AdaptiveStochasticEpisodeBatchSource.selfConsistentScheduledEpisodes mdp varianceProxy baseVisitFloor t)) : selfConsistentScheduledNaturalCausalStoppingTimeAverageRealizedBehaviorRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory = selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory - selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix scheduleIndex trajectory
Source line 313 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_tendstoAlmostEverywhere_zero Compiled

The exact uncapped stopped behavior expected-regret process converges almost everywhere.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_tendstoAlmostEverywhere_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_tendstoAlmostEverywhere_zero (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) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix ∀ᵐ trajectory ∂source.trajectoryMeasure, Tendsto (fun scheduleIndex => behaviorProcess scheduleIndex trajectory) atTop (nhds 0)
Source line 350 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret Compiled

Every exact uncapped stopped behavior expected-regret coordinate is measurable.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.measurable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem measurable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (scheduleIndex : Nat) : Measurable (selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor) scheduleIndex)
Source line 396 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integrable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret Compiled

Every exact uncapped stopped behavior expected-regret coordinate is integrable by the global `2H` envelope.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integrable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem integrable_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (scheduleIndex : Nat) : Integrable (selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor) scheduleIndex) (selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor).trajectoryMeasure
Source line 422 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret Compiled

Every exact uncapped stopped behavior expected-regret coordinate belongs to `L1`.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (scheduleIndex : Nat) : MemLp (selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor (selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor) scheduleIndex) 1 (selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor).trajectoryMeasure
Source line 468 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integral_abs_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero Compiled

Expected absolute stopped behavior expected regret tends to zero.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.integral_abs_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem integral_abs_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero (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) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (fun trajectory => |behaviorProcess scheduleIndex trajectory|)) atTop (nhds 0)
Source line 496 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_eq Compiled

Exponent-one norm of the exact stopped behavior expected-regret process is the lifted expected absolute value.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_eq

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_eq (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (rewardSource : mdp.MeanCompatibleRewardKernel) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (varianceProxy : NNReal) (baseVisitFloor : Real) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (scheduleIndex : Nat) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix eLpNorm (behaviorProcess scheduleIndex) 1 source.trajectoryMeasure = ENNReal.ofReal (integral source.trajectoryMeasure (fun trajectory => |behaviorProcess scheduleIndex trajectory|))
Source line 591 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero Compiled

The exact stopped behavior expected-regret process converges to zero in exponent-one norm.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegret_tendsto_zero (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) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix Tendsto (fun scheduleIndex => eLpNorm (behaviorProcess scheduleIndex) 1 source.trajectoryMeasure) atTop (nhds 0)
Source line 624 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegretIntegral_tendsto_zero Compiled

Signed expectation of the exact stopped behavior expected-regret process tends to zero.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegretIntegral_tendsto_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedBehaviorExpectedRegretIntegral_tendsto_zero (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) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (behaviorProcess scheduleIndex)) atTop (nhds 0)
Source line 667 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation Compiled

The exact uncapped stopped return-deviation coordinates belong to `L1`.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem memLp_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation (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 : 4 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) (scheduleIndex : Nat) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let returnProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix MemLp (returnProcess scheduleIndex) 1 source.trajectoryMeasure
Source line 708 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation_tendsto_zero Compiled

Exponent-one norm of the exact stopped return deviation tends to zero.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation_tendsto_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem eLpNorm_one_selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviation_tendsto_zero (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 : 4 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let returnProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix Tendsto (fun scheduleIndex => eLpNorm (returnProcess scheduleIndex) 1 source.trajectoryMeasure) atTop (nhds 0)
Source line 784 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviationIntegral_tendsto_zero Compiled

Signed expectation of the stopped return deviation tends to zero.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviationIntegral_tendsto_zero

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppedReturnDeviationIntegral_tendsto_zero (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 : 4 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let returnProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (returnProcess scheduleIndex)) atTop (nhds 0)
Source line 896 Chapter context: Finite-horizon RL
theorem BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_and_returnDeviation_L1_consistency Compiled

Terminal policy-value semantic and `L1` package at genuine uncapped `hittingAfter`.

Used in these reading views: Bandit Book · Reinforcement Learning Book

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveStochasticSampledEmpiricalOptimisticSource.selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_and_returnDeviation_L1_consistency

Reading membership is not a proof dependency. Exact assumptions remain in the Lean statement.

theorem selfConsistentScheduledCausalSource_inverseSqrtThresholdUnboundedHittingAfter_stoppedBehaviorExpectedRegret_and_returnDeviation_L1_consistency (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 : 4 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) : let source := selfConsistentScheduledCausalSource mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let stoppingPrefix := selfConsistentScheduledNaturalCausalInverseSqrtThresholdUnboundedHittingAfterStoppingPrefix mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor let behaviorProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageBehaviorExpectedRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix let realizedProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageRealizedBehaviorRegretProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix let returnProcess := selfConsistentScheduledNaturalCausalStoppingTimeAverageReturnDeviationProcess mdp initialState rewardSource initialTable defaultState varianceProxy baseVisitFloor stoppingPrefix (forall scheduleIndex, Measurable (behaviorProcess scheduleIndex) /\ MemLp (behaviorProcess scheduleIndex) 1 source.trajectoryMeasure /\ MemLp (returnProcess scheduleIndex) 1 source.trajectoryMeasure) /\ (forall scheduleIndex trajectory, 0 <= behaviorProcess scheduleIndex trajectory /\ behaviorProcess scheduleIndex trajectory <= 2 * (mdp.horizon : Real) /\ realizedProcess scheduleIndex trajectory = behaviorProcess scheduleIndex trajectory - returnProcess scheduleIndex trajectory) /\ (∀ᵐ trajectory ∂source.trajectoryMeasure, Tendsto (fun scheduleIndex => behaviorProcess scheduleIndex trajectory) atTop (nhds 0)) /\ Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (fun trajectory => |behaviorProcess scheduleIndex trajectory|)) atTop (nhds 0) /\ Tendsto (fun scheduleIndex => eLpNorm (behaviorProcess scheduleIndex) 1 source.trajectoryMeasure) atTop (nhds 0) /\ Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (behaviorProcess scheduleIndex)) atTop (nhds 0) /\ Tendsto (fun scheduleIndex => eLpNorm (returnProcess scheduleIndex) 1 source.trajectoryMeasure) atTop (nhds 0) /\ Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (returnProcess scheduleIndex)) atTop (nhds 0) /\ (forall scheduleIndex, integral source.trajectoryMeasure (realizedProcess scheduleIndex) = integral source.trajectoryMeasure (behaviorProcess scheduleIndex) - integral source.trajectoryMeasure (returnProcess scheduleIndex)) /\ Tendsto (fun scheduleIndex => integral source.trajectoryMeasure (realizedProcess scheduleIndex)) atTop (nhds 0)
Source line 939 Chapter context: Finite-horizon RL