Lean module · EXP3
BanditRLProof.Exp3MixedSquareExponentialRealizedTuning
# Learning-rate tuning for exponential-square realized EXP3 regret The exponential mixed-square route replaces the Markov square threshold by `K * T + sampledMixedSquaredConfidenceRadius`. This module chooses the exact learning rate `eta = sqrt (log K / (K * T + squareRadius))` at the public four-event allocation. It balances entropy against the complete exponential-square stability scale. The exploration, two Bernstein, and realized-deviation contributions remain explicit for a later gamma schedule.
Module map
Imports
BanditRLProof.Exp3MixedSquareExponentialRealizedHighProbabilityRegret
Imported by
BanditRLProof, BanditRLProof.Exp3MixedSquareExponentialRealizedExplicitTuning
Declarations
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def
BanditRLProof.Exp3.exponentialSquareHighProbabilityScale
Compiled
Positive scale appearing in the exponential-square Hedge term when the square event receives `delta / 4`.
noncomputable def exponentialSquareHighProbabilityScale {Action : Type v} (arms : Finset Action) (gamma : Real) (horizon : Nat) (delta : Real) : Real
theorem
BanditRLProof.Exp3.exponentialSquareHighProbabilityScale_pos
Compiled
No declaration docstring is present; use the chapter context and exact statement below.
theorem exponentialSquareHighProbabilityScale_pos {Action : Type v} (arms : Finset Action) (hcard_two : 2 <= arms.card) (gamma : Real) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) : 0 < exponentialSquareHighProbabilityScale arms gamma horizon delta
def
BanditRLProof.Exp3.exponentialSquareHighProbabilityLearningRate
Compiled
Learning rate balancing entropy against the complete exponential-square stability scale at the public `delta / 4` square allocation.
noncomputable def exponentialSquareHighProbabilityLearningRate {Action : Type v} (arms : Finset Action) (gamma : Real) (horizon : Nat) (delta : Real) : Real
theorem
BanditRLProof.Exp3.exponentialSquareHighProbabilityLearningRate_pos
Compiled
No declaration docstring is present; use the chapter context and exact statement below.
theorem exponentialSquareHighProbabilityLearningRate_pos {Action : Type v} (arms : Finset Action) (hcard_two : 2 <= arms.card) (gamma : Real) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) : 0 < exponentialSquareHighProbabilityLearningRate arms gamma horizon delta
theorem
BanditRLProof.Exp3.exponentialSquareHighProbabilityLearningRate_sq_mul_scale
Compiled
No declaration docstring is present; use the chapter context and exact statement below.
theorem exponentialSquareHighProbabilityLearningRate_sq_mul_scale {Action : Type v} (arms : Finset Action) (hcard_two : 2 <= arms.card) (gamma : Real) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) : exponentialSquareHighProbabilityLearningRate arms gamma horizon delta ^ 2 * exponentialSquareHighProbabilityScale arms gamma horizon delta = Real.log (arms.card : Real)
theorem
BanditRLProof.Exp3.exponentialSquareHighProbabilityHedgeBudget_le_three_mul_sqrt
Compiled
With `gamma <= 1/2`, entropy and the stability-amplified exponential-square scale cost at most three copies of their balanced square-root scale.
theorem exponentialSquareHighProbabilityHedgeBudget_le_three_mul_sqrt {Action : Type v} (arms : Finset Action) (hcard_two : 2 <= arms.card) (gamma : Real) (hgamma_pos : 0 < gamma) (hgamma_le_half : gamma <= 1 / 2) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) : Real.log (arms.card : Real) / exponentialSquareHighProbabilityLearningRate arms gamma horizon delta + (exponentialSquareHighProbabilityLearningRate arms gamma horizon delta * (1 / (1 - gamma))) * exponentialSquareHighProbabilityScale arms gamma horizon delta <= 3 * Real.sqrt (Real.log (arms.card : Real) * exponentialSquareHighProbabilityScale arms gamma horizon delta)
def
BanditRLProof.Exp3.exponentialSquareBernsteinRealizedTunedThreshold
Compiled
Explicit threshold after tuning all learning-rate-dependent terms. The exploration and three confidence contributions remain visible.
noncomputable def exponentialSquareBernsteinRealizedTunedThreshold {Action : Type v} (arms : Finset Action) (gamma : Real) (horizon : Nat) (delta : Real) : Real
theorem
BanditRLProof.Exp3.sampledPredictableExponentialSquareBernsteinRealizedHighProbabilityRegretBudget_le_tunedThreshold
Compiled
The complete exponential-square four-event realized budget is bounded by the learning-rate-tuned threshold.
theorem sampledPredictableExponentialSquareBernsteinRealizedHighProbabilityRegretBudget_le_tunedThreshold {Action : Type v} [DecidableEq Action] (arms : Finset Action) (hcard_two : 2 <= arms.card) (gamma : Real) (hgamma_pos : 0 < gamma) (hgamma_le_half : gamma <= 1 / 2) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) : sampledPredictableExponentialSquareBernsteinRealizedHighProbabilityRegretBudget arms (exponentialSquareHighProbabilityLearningRate arms gamma horizon delta) gamma horizon (delta / 4) (delta / 4) (delta / 4) <= exponentialSquareBernsteinRealizedTunedThreshold arms gamma horizon delta
theorem
BanditRLProof.Exp3.sampledPredictable_tunedExponentialSquareBernsteinRealizedRegret_tail
Compiled
Generated realized-regret tail with the exponential-square-balanced learning rate. This removes the old Markov tuning and leaves only the gamma schedule and confidence-radius simplification for downstream consumers.
theorem sampledPredictable_tunedExponentialSquareBernsteinRealizedRegret_tail {Env : Type u} {Action : Type v} [MeasurableSpace Env] [StandardBorelSpace Env] [Nonempty Env] [MeasurableSpace Action] [MeasurableSingletonClass Action] [StandardBorelSpace Action] [Nonempty Action] [DecidableEq Action] (prior : Measure Env) [IsProbabilityMeasure prior] (arms : Finset Action) (harms : arms.Nonempty) (hcard_two : 2 <= arms.card) (gamma : Real) (hgamma_pos : 0 < gamma) (hgamma_le_half : gamma <= 1 / 2) (loss : PredictableLossVector Env Action) (comparator : Action) (hcomparator : comparator ∈ arms) (horizon : Nat) (hhorizon : 0 < horizon) (delta : Real) (hdelta : 0 < delta) : let eta := exponentialSquareHighProbabilityLearningRate arms gamma horizon delta let mu := prior ⊗ₘ sampledImportanceWeightedTrajectoryKernel arms harms eta gamma hgamma_pos.le (by linarith : gamma <= 1) loss.environment mu {sample | exponentialSquareBernsteinRealizedTunedThreshold arms gamma horizon delta <= (Finset.range horizon).sum (fun t => sampledTrajectoryRealizedLossAt t sample) - (Finset.range horizon).sum (fun t => predictableLossAt loss t sample comparator)} <= ENNReal.ofReal delta