BanditRLlib
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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.

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Imports

BanditRLProof.RL.FiniteHorizonIIDCountConcentration, BanditRLProof.ProbabilityUnionBound

Imported by

BanditRLProof, BanditRLProof.RL.FiniteHorizonIIDEligibleVisitCountPositivity

Declarations

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

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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.equivVisitSumTransition

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.countCoordinateCard

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.countCoordinateCard_eq

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.simultaneousCountDelta

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.simultaneousCountConfidenceRadius

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.deviation

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.measurable_deviation

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.badEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.CountCoordinate.measurableSet_badEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.measure_countCoordinate_badEvent_le

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountBadEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.measurableSet_simultaneousCountBadEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_pos

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.simultaneousCountDelta_le_one

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneousCountBadEvent_le

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.countCoordinate_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.visitCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.transitionCount_abs_deviation_lt_of_not_mem_simultaneousCountBadEvent

Reading 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 identitydeclaration:BanditRLProof.FiniteHorizonRL.MarkovPolicy.iidEpisodeBatch_simultaneous_count_confidence

Reading 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