Self-normalised deviation bound, uniform over the pull count
ProvedBanditAlgorithm.bandit_selfnormalised_union_countsbanditsconcentrationgaussian
The self-normalised deviation bound, uniform over the pull count: for a unit-variance Gaussian bandit, an arbitrary policy, an arm and ,
equivalently .
The hypothesis is not a defect: when arm has never been played both sides of the deviation inequality vanish, so the case carries no information, and in the application excludes it. The proof is a union over the possible values of , each handled at its own optimal tilt. The factor is the price of that union; removing it -- which the threshold of Lemma 33.7 requires, since is logarithmic rather than linear in -- is what the mixture martingale is for.
Preamble
import Definitions.Def_TrackAndStop import Definitions.Def_GaussianBandit open MeasureTheory ProbabilityTheory NNReal ENNReal open scoped Classical
Formal statement
theorem BanditAlgorithm.bandit_selfnormalised_union_counts {k : ℕ} (μvec : Fin k → ℝ)
(pol : BanditAlgorithm.BanditPolicy k) (a : Fin k) (n : ℕ) {β : ℝ} (hβ : 0 < β) :
BanditAlgorithm.banditTrajMeasure (BanditAlgorithm.gaussianBandit μvec) pol
{ω : ℕ → Fin k × ℝ | 0 < BanditAlgorithm.trajPullCount a n ω ∧
2 * (BanditAlgorithm.trajPullCount a n ω : ℝ) * β
≤ ((∑ s ∈ (Finset.range n).filter fun s ↦ (ω s).1 = a, (ω s).2)
- (BanditAlgorithm.trajPullCount a n ω : ℝ) * μvec a) ^ 2}
≤ (n : ENNReal) * (2 * ENNReal.ofReal (Real.exp (-β))) := by
sorrySource
Union bound over the possible pull counts of an arm; standard, see Garivier & Kaufmann, COLT 2016, Section 4. Superseded for time-uniform purposes by the mixture martingale, whose bound carries no factor growing with the horizon.