Lean module · Finite-horizon RL
BanditRLProof.RL.FiniteHorizonIIDSimultaneousCountConfidence
This module puts every finite visit and joint-transition count coordinate into one index type and applies an equal-share finite union bound to the compiled fixed-coordinate tails. The route remains fixed-policy iid. It does not form visit-conditioned ratios or claim adaptive, anytime, or cumulative regret.
Module map
Imports
BanditRLProof.RL.FiniteHorizonIIDCountConcentration, BanditRLProof.ProbabilityUnionBound
Imported by
BanditRLProof, BanditRLProof.RL.FiniteHorizonIIDEligibleVisitCountPositivity
Declarations
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inductive
BanditRLProof.FiniteHorizonRL.CountCoordinate
Compiled
Finite index of all visit and joint-transition count coordinates of an MDP.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinateReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
inductive CountCoordinate (mdp : MDP State Action) where
def
BanditRLProof.FiniteHorizonRL.CountCoordinate.equivVisitSumTransition
Compiled
Explicit finite-sum presentation of the two coordinate families.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.equivVisitSumTransitionReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
def equivVisitSumTransition (mdp : MDP State Action) : CountCoordinate mdp ≃ (Fin mdp.horizon × State × Action) ⊕ (Fin mdp.horizon × State × Action × State) where
def
BanditRLProof.FiniteHorizonRL.countCoordinateCard
Compiled
Number of visit and joint-transition coordinates in the simultaneous family.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.countCoordinateCardReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
def countCoordinateCard (mdp : MDP State Action) : Nat
theorem
BanditRLProof.FiniteHorizonRL.countCoordinateCard_eq
Compiled
The simultaneous family contains `H*S*A` visits and `H*S*A*S` transitions.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.countCoordinateCard_eqReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem countCoordinateCard_eq (mdp : MDP State Action) : countCoordinateCard mdp = mdp.horizon * Fintype.card State * Fintype.card Action + mdp.horizon * Fintype.card State * Fintype.card Action * Fintype.card State
def
BanditRLProof.FiniteHorizonRL.simultaneousCountDelta
Compiled
Equal confidence share assigned to each count coordinate.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.simultaneousCountDeltaReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def simultaneousCountDelta (mdp : MDP State Action) (delta : Real) : Real
def
BanditRLProof.FiniteHorizonRL.simultaneousCountConfidenceRadius
Compiled
Common count radius after allocating the global confidence budget equally.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.simultaneousCountConfidenceRadiusReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def simultaneousCountConfidenceRadius (mdp : MDP State Action) (episodes : Nat) (delta : Real) : Real
def
BanditRLProof.FiniteHorizonRL.CountCoordinate.deviation
Compiled
Real count deviation selected by a visit or joint-transition coordinate.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.deviationReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def deviation {mdp : MDP State Action} (coordinate : CountCoordinate mdp) (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] {episodes : Nat} (batch : EpisodeBatch mdp episodes) : Real
theorem
BanditRLProof.FiniteHorizonRL.CountCoordinate.measurable_deviation
Compiled
Every selected count deviation is measurable on the mapped batch space.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.measurable_deviationReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem measurable_deviation {mdp : MDP State Action} (coordinate : CountCoordinate mdp) (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] {episodes : Nat} : Measurable (coordinate.deviation policy initialState : EpisodeBatch mdp episodes → Real)
def
BanditRLProof.FiniteHorizonRL.CountCoordinate.badEvent
Compiled
Two-sided bad event for one selected coordinate at a supplied delta.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.badEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def badEvent {mdp : MDP State Action} (coordinate : CountCoordinate mdp) (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (coordinateDelta : Real) : Set (EpisodeBatch mdp episodes)
theorem
BanditRLProof.FiniteHorizonRL.CountCoordinate.measurableSet_badEvent
Compiled
Every selected fixed-coordinate bad event is measurable.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.measurableSet_badEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem measurableSet_badEvent {mdp : MDP State Action} (coordinate : CountCoordinate mdp) (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (coordinateDelta : Real) : MeasurableSet (coordinate.badEvent policy initialState episodes coordinateDelta)
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.measure_countCoordinate_badEvent_le
Compiled
The compiled marginal tail dispatches over the finite coordinate type.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.measure_countCoordinate_badEvent_leReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem measure_countCoordinate_badEvent_le {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (hepisodes : 0 < episodes) (coordinate : CountCoordinate mdp) (coordinateDelta : Real) (hdelta : 0 < coordinateDelta) (hdelta_le_one : coordinateDelta ≤ 1) : (policy.iidEpisodeBatchMeasure initialState episodes) (coordinate.badEvent policy initialState episodes coordinateDelta) ≤ ENNReal.ofReal coordinateDelta
def
BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountBadEvent
Compiled
Union of every visit and joint-transition count bad event.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountBadEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
noncomputable def simultaneousCountBadEvent {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (delta : Real) : Set (EpisodeBatch mdp episodes)
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.measurableSet_simultaneousCountBadEvent
Compiled
The simultaneous count bad event is measurable.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.measurableSet_simultaneousCountBadEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem measurableSet_simultaneousCountBadEvent {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (delta : Real) : MeasurableSet (policy.simultaneousCountBadEvent initialState episodes delta)
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_pos
Compiled
A nonempty coordinate family receives a positive equal delta share.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_posReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem simultaneousCountDelta_pos {mdp : MDP State Action} (hcoordinate : Nonempty (CountCoordinate mdp)) {delta : Real} (hdelta : 0 < delta) : 0 < simultaneousCountDelta mdp delta
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_le_one
Compiled
A global delta at most one gives every nonempty-family share at most one.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_le_oneReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem simultaneousCountDelta_le_one {mdp : MDP State Action} (hcoordinate : Nonempty (CountCoordinate mdp)) {delta : Real} (hdelta : 0 < delta) (hdelta_le_one : delta ≤ 1) : simultaneousCountDelta mdp delta ≤ 1
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneousCountBadEvent_le
Compiled
All finite visit and joint-transition count deviations share one global delta budget. When the coordinate family is empty (in particular at horizon zero), the bad union is empty and no positive-horizon premise is needed.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneousCountBadEvent_leReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem iidEpisodeBatch_simultaneousCountBadEvent_le {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (hepisodes : 0 < episodes) (delta : Real) (hdelta : 0 < delta) (hdelta_le_one : delta ≤ 1) : (policy.iidEpisodeBatchMeasure initialState episodes) (policy.simultaneousCountBadEvent initialState episodes delta) ≤ ENNReal.ofReal delta
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.countCoordinate_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent
Compiled
Outside the simultaneous union, every indexed deviation is below its radius.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.countCoordinate_abs_deviation_lt_of_not_mem_simultaneousCountBadEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem countCoordinate_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] {episodes : Nat} {delta : Real} (batch : EpisodeBatch mdp episodes) (hbatch : batch ∉ policy.simultaneousCountBadEvent initialState episodes delta) (coordinate : CountCoordinate mdp) : |coordinate.deviation policy initialState batch| < simultaneousCountConfidenceRadius mdp episodes delta
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.visitCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent
Compiled
Visit-count specialization of the simultaneous good-side bound.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.visitCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem visitCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] {episodes : Nat} {delta : Real} (batch : EpisodeBatch mdp episodes) (hbatch : batch ∉ policy.simultaneousCountBadEvent initialState episodes delta) (stage : Fin mdp.horizon) (state : State) (action : Action) : |(batch.visitCount stage state action : Real) - (episodes : Real) * policy.stageVisitProbability initialState stage state action| < simultaneousCountConfidenceRadius mdp episodes delta
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.transitionCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent
Compiled
Joint-transition-count specialization of the simultaneous good-side bound.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.transitionCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEventReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem transitionCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] {episodes : Nat} {delta : Real} (batch : EpisodeBatch mdp episodes) (hbatch : batch ∉ policy.simultaneousCountBadEvent initialState episodes delta) (stage : Fin mdp.horizon) (state : State) (action : Action) (nextState : State) : |(batch.transitionCount stage state action nextState : Real) - (episodes : Real) * policy.stageTransitionJointProbability initialState stage state action nextState| < simultaneousCountConfidenceRadius mdp episodes delta
theorem
BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneous_count_confidence
Compiled
Route endpoint: one global-delta bad union and all coordinatewise good-side bounds under the same mapped fixed-policy iid episode-batch law.
Used in these reading views: Bandit Book · Reinforcement Learning Book
9. Finite-horizon reinforcement learning
Canonical node identity
declaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneous_count_confidenceReading membership is not a proof dependency. Exact assumptions remain in the Lean statement.
theorem iidEpisodeBatch_simultaneous_count_confidence {mdp : MDP State Action} (policy : MarkovPolicy mdp) (initialState : Measure State) [IsProbabilityMeasure initialState] (episodes : Nat) (hepisodes : 0 < episodes) (delta : Real) (hdelta : 0 < delta) (hdelta_le_one : delta ≤ 1) : (policy.iidEpisodeBatchMeasure initialState episodes) (policy.simultaneousCountBadEvent initialState episodes delta) ≤ ENNReal.ofReal delta ∧ ∀ batch ∉ policy.simultaneousCountBadEvent initialState episodes delta, ∀ coordinate : CountCoordinate mdp, |coordinate.deviation policy initialState batch| < simultaneousCountConfidenceRadius mdp episodes delta