BanditRLlib
Lean gate passed before this site build; local proof declarations are shown as compiled.Lean-verified build · exact declarations linked.

Lean module · Finite-horizon RL

BanditRLProof.RL.FiniteHorizonAdaptiveCumulativeEpisodewiseCommonSpaceConsistency

The preceding episodewise route gives one sharp certificate for every scheduled finite window, but those windows have different trajectory types. This module places the finite-window laws on one dependent infinite product. Coordinate n therefore has exactly the compiled adaptive trajectory law for schedule n, and the scheduled realized-regret coordinates form one random process.

Module map

Declarations
17
Placeholders
0

Imports

BanditRLProof.RL.FiniteHorizonAdaptiveCumulativeEpisodewiseRealizedBehaviorConsistency

Imported by

BanditRLProof, BanditRLProof.RL.FiniteHorizonAdaptiveCumulativeEpisodewiseCommonSpaceExpectedConsistency

Declarations

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

theorem BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.measurable_realizedSuccessorCumulativeRegret Compiled

Realized cumulative successor regret is measurable in the finite trajectory.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.measurable_realizedSuccessorCumulativeRegret

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

theorem measurable_realizedSuccessorCumulativeRegret {mdp : MDP State Action} {initialState : Measure State} [IsProbabilityMeasure initialState] {episodes : Nat} (source : AdaptiveEpisodeBatchSource mdp initialState episodes) (rounds : Nat) : Measurable (fun trajectory => source.realizedSuccessorCumulativeRegret trajectory rounds)
theorem BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.measurable_realizedSuccessorAverageRegret Compiled

Realized average successor regret is measurable in the finite trajectory.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.measurable_realizedSuccessorAverageRegret

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

theorem measurable_realizedSuccessorAverageRegret {mdp : MDP State Action} {initialState : Measure State} [IsProbabilityMeasure initialState] {episodes : Nat} (source : AdaptiveEpisodeBatchSource mdp initialState episodes) (rounds : Nat) : Measurable (fun trajectory => source.realizedSuccessorAverageRegret trajectory rounds)
theorem BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.successorExpectedCumulativeRegret_nonneg Compiled

A finite sum of policy expected regrets is nonnegative.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.successorExpectedCumulativeRegret_nonneg

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

theorem successorExpectedCumulativeRegret_nonneg {mdp : MDP State Action} {initialState : Measure State} [IsProbabilityMeasure initialState] {episodes : Nat} (source : AdaptiveEpisodeBatchSource mdp initialState episodes) (trajectory : EpisodeBatchTrajectory mdp episodes) (rounds : Nat) : 0 <= source.successorExpectedCumulativeRegret trajectory rounds
theorem BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.successorExpectedAverageRegret_nonneg Compiled

The average of successor policy expected regrets is nonnegative.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.successorExpectedAverageRegret_nonneg

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

theorem successorExpectedAverageRegret_nonneg {mdp : MDP State Action} {initialState : Measure State} [IsProbabilityMeasure initialState] {episodes : Nat} (source : AdaptiveEpisodeBatchSource mdp initialState episodes) (trajectory : EpisodeBatchTrajectory mdp episodes) (rounds : Nat) : 0 <= source.successorExpectedAverageRegret trajectory rounds
theorem BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.abs_realizedSuccessorAverageRegret_le_of_expected_le_of_deviation_abs_le Compiled

An expected-regret upper bound and a two-sided return-deviation bound control the absolute realized average regret.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveEpisodeBatchSource.abs_realizedSuccessorAverageRegret_le_of_expected_le_of_deviation_abs_le

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

theorem abs_realizedSuccessorAverageRegret_le_of_expected_le_of_deviation_abs_le {mdp : MDP State Action} {initialState : Measure State} [IsProbabilityMeasure initialState] {episodes : Nat} (source : AdaptiveEpisodeBatchSource mdp initialState episodes) (trajectory : EpisodeBatchTrajectory mdp episodes) (rounds : Nat) (hrounds : 0 < rounds) (hepisodes : 0 < episodes) (expectedBound deviationBound : Real) (hexpected : source.successorExpectedAverageRegret trajectory rounds <= expectedBound) (hdeviation : |source.cumulativeSuccessorReturnDeviation rounds trajectory| <= deviationBound) : |source.realizedSuccessorAverageRegret trajectory rounds| <= expectedBound + deviationBound / ((episodes : Real) * (rounds : Real))
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.exploratorySource_trajectoryMeasure_cumulativeInverseSqrtPathSupport_optimism_and_decayingExplorationEpisodewiseAverageAbsoluteRealizedBehaviorConsistency Compiled

The sharp finite-window certificate controls the absolute realized regret, not only its upper tail. This is the finite-window input needed by convergence in probability.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.exploratorySource_trajectoryMeasure_cumulativeInverseSqrtPathSupport_optimism_and_decayingExplorationEpisodewiseAverageAbsoluteRealizedBehaviorConsistency

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

theorem exploratorySource_trajectoryMeasure_cumulativeInverseSqrtPathSupport_optimism_and_decayingExplorationEpisodewiseAverageAbsoluteRealizedBehaviorConsistency (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (baseVisitFloor : Real) (n : Nat) [StandardBorelSpace (EpisodeBatch mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))] [StandardBorelSpace (EpisodeBatchTrajectory mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) : let rounds := AdaptiveEpisodeBatchSource.decayingExplorationRounds mdp n let delta := AdaptiveEpisodeBatchSource.vanishingAverageConfidenceDelta n let explorationRate := AdaptiveEpisodeBatchSource.decayingExplorationRate n let visitFloor := AdaptiveEpisodeBatchSource.decayingExplorationVisitFloor mdp baseVisitFloor n let episodes := AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n let countRadius := AdaptiveEpisodeBatchSource.normalizedCumulativeInverseSqrtCountRadius mdp rounds delta visitFloor let source := exploratorySource mdp initialState episodes initialTable defaultState countRadius explorationRate (AdaptiveEpisodeBatchSource.decayingExplorationRate_le_one n) let countBadEvent := source.adaptiveCumulativeCountBadEvent rounds delta let returnBadEvent := source.episodewiseSuccessorReturnDeviationBadEvent rounds delta let combinedBadEvent := countBadEvent ∪ returnBadEvent MeasurableSet combinedBadEvent /\ source.trajectoryMeasure combinedBadEvent <= AdaptiveEpisodeBatchSource.decayingExplorationRealizedFailureBudget n /\ forall trajectory, trajectory ∉ combinedBadEvent -> (forall round : Fin rounds, forall state, mdp.optimalValueRemaining mdp.horizon le_rfl state <= (adaptiveCumulativeEmpiricalOptimisticPlanAt trajectory defaultState countRadius round).upperValueRemaining mdp.horizon le_rfl state) /\ |source.realizedSuccessorAverageRegret trajectory rounds| <= AdaptiveEpisodeBatchSource.decayingExplorationEpisodewiseAverageRealizedBehaviorRegretBound mdp baseVisitFloor n
abbrev BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.DecayingExplorationEpisodewiseWindowSpace Compiled

The dependent sample space containing one complete scheduled experiment per coordinate.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.DecayingExplorationEpisodewiseWindowSpace

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

abbrev DecayingExplorationEpisodewiseWindowSpace (mdp : MDP State Action) (baseVisitFloor : Real)
def BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseWindowSource Compiled

The concrete adaptive source used at schedule coordinate `n`.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseWindowSource

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

noncomputable def decayingExplorationEpisodewiseWindowSource (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) : AdaptiveEpisodeBatchSource mdp initialState (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n)
def BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseWindowMeasure Compiled

The scheduled adaptive trajectory law at common-space coordinate `n`.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseWindowMeasure

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

noncomputable def decayingExplorationEpisodewiseWindowMeasure (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) : Measure (EpisodeBatchTrajectory mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))
def BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure Compiled

Independent-coordinate coupling of all scheduled finite-window trajectory laws.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure

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

noncomputable def decayingExplorationEpisodewiseCommonMeasure (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) : Measure (DecayingExplorationEpisodewiseWindowSpace mdp baseVisitFloor)
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure_map_eval Compiled

Every common-space coordinate has exactly its scheduled adaptive law.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure_map_eval

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

theorem decayingExplorationEpisodewiseCommonMeasure_map_eval (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) : (decayingExplorationEpisodewiseCommonMeasure mdp initialState initialTable defaultState baseVisitFloor).map (fun omega => omega n) = decayingExplorationEpisodewiseWindowMeasure mdp initialState initialTable defaultState baseVisitFloor n
def BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseRealizedBehaviorRegretProcess Compiled

Scheduled realized successor-average regret as one common-space process.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseRealizedBehaviorRegretProcess

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

noncomputable def decayingExplorationEpisodewiseRealizedBehaviorRegretProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) (omega : DecayingExplorationEpisodewiseWindowSpace mdp baseVisitFloor) : Real
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.measurable_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess Compiled

Every scheduled regret coordinate is a measurable real random variable.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.measurable_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess

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

theorem measurable_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) : Measurable (decayingExplorationEpisodewiseRealizedBehaviorRegretProcess mdp initialState initialTable defaultState baseVisitFloor n)
def BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonBadEvent Compiled

Pull the sharp count/return union at coordinate `n` to the common space.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonBadEvent

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

noncomputable def decayingExplorationEpisodewiseCommonBadEvent (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (baseVisitFloor : Real) (n : Nat) : Set (DecayingExplorationEpisodewiseWindowSpace mdp baseVisitFloor)
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure_badEvent_le Compiled

The pulled-back coordinate bad event inherits the exact finite-window budget.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.decayingExplorationEpisodewiseCommonMeasure_badEvent_le

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

theorem decayingExplorationEpisodewiseCommonMeasure_badEvent_le (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (baseVisitFloor : Real) (hbatchBorel : forall n, StandardBorelSpace (EpisodeBatch mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (htrajectoryBorel : forall n, StandardBorelSpace (EpisodeBatchTrajectory mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) (n : Nat) : decayingExplorationEpisodewiseCommonMeasure mdp initialState initialTable defaultState baseVisitFloor (decayingExplorationEpisodewiseCommonBadEvent mdp initialState initialTable defaultState baseVisitFloor n) <= AdaptiveEpisodeBatchSource.decayingExplorationRealizedFailureBudget n
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.abs_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess_le_of_not_mem_badEvent Compiled

Outside the pulled-back bad event, coordinate `n` has the sharp absolute bound.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.abs_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess_le_of_not_mem_badEvent

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

theorem abs_decayingExplorationEpisodewiseRealizedBehaviorRegretProcess_le_of_not_mem_badEvent (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (baseVisitFloor : Real) (hbatchBorel : forall n, StandardBorelSpace (EpisodeBatch mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (htrajectoryBorel : forall n, StandardBorelSpace (EpisodeBatchTrajectory mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) (n : Nat) (omega : DecayingExplorationEpisodewiseWindowSpace mdp baseVisitFloor) (homega : omega ∉ decayingExplorationEpisodewiseCommonBadEvent mdp initialState initialTable defaultState baseVisitFloor n) : |decayingExplorationEpisodewiseRealizedBehaviorRegretProcess mdp initialState initialTable defaultState baseVisitFloor n omega| <= AdaptiveEpisodeBatchSource.decayingExplorationEpisodewiseAverageRealizedBehaviorRegretBound mdp baseVisitFloor n
theorem BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.exploratorySource_decayingExplorationEpisodewiseCommonMeasure_marginals_and_realizedBehaviorRegret_tendstoInMeasure_zero Compiled

Terminal common-space theorem: measurable regret coordinates, exact scheduled marginals, and convergence in probability of the process to zero.

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

9. Finite-horizon reinforcement learning

Canonical node identitydeclaration:BanditRLProof.FiniteHorizonRL.AdaptiveCumulativeEmpiricalOptimisticSource.exploratorySource_decayingExplorationEpisodewiseCommonMeasure_marginals_and_realizedBehaviorRegret_tendstoInMeasure_zero

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

theorem exploratorySource_decayingExplorationEpisodewiseCommonMeasure_marginals_and_realizedBehaviorRegret_tendstoInMeasure_zero (mdp : MDP State Action) (initialState : Measure State) [IsProbabilityMeasure initialState] (baseVisitFloor : Real) (hbatchBorel : forall n, StandardBorelSpace (EpisodeBatch mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (htrajectoryBorel : forall n, StandardBorelSpace (EpisodeBatchTrajectory mdp (AdaptiveEpisodeBatchSource.decayingExplorationScheduledEpisodes mdp baseVisitFloor n))) (initialTable : DeterministicMarkovPolicyTable mdp) (defaultState : State) (support : ExploratoryPathSupport mdp initialState) (hbaseFloor : ExploratoryPathUniformVisitFloor support 1 baseVisitFloor) (hrewardBound : forall state action, |mdp.reward state action| <= 1) (hhorizon : 0 < mdp.horizon) (hbaseVisitFloor : 0 < baseVisitFloor) : (forall n, Measurable (decayingExplorationEpisodewiseRealizedBehaviorRegretProcess mdp initialState initialTable defaultState baseVisitFloor n)) /\ (forall n, (decayingExplorationEpisodewiseCommonMeasure mdp initialState initialTable defaultState baseVisitFloor).map (fun omega => omega n) = decayingExplorationEpisodewiseWindowMeasure mdp initialState initialTable defaultState baseVisitFloor n) /\ TendstoInMeasure (decayingExplorationEpisodewiseCommonMeasure mdp initialState initialTable defaultState baseVisitFloor) (decayingExplorationEpisodewiseRealizedBehaviorRegretProcess mdp initialState initialTable defaultState baseVisitFloor) atTop (fun _ => 0)