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A universally optimal action has zero partial-monitoring minimax regret

Proved
BanditAlgorithm.pmMinimaxRegret_eq_zero_of_universally_optimal

by Harry_Xu · Aug 13, 2026 · Mathlib c5ea003 (Lean v4.30.0)

banditsminimax-regretpartial-monitoring

Let GGG be a finite partial-monitoring game. Suppose there is an action aaa whose loss is no larger than the loss of any action bbb on every outcome iii: Lai≤LbiL_{a i} \le L_{b i}Lai​≤Lbi​. Then, for every horizon nnn,

Rn∗(G)=0.R_n^*(G)=0.Rn∗​(G)=0.

Indeed, always playing aaa achieves zero worst-case regret, while comparison with aaa makes every policy's worst-case regret nonnegative. This lemma isolates the decision-theoretic part of the zero-regret case and is reusable independently of the cell geometry.

Formalization Note The statement includes the discrete-signal measurability assumptions needed to construct and evaluate the deterministic constant policy.

Preamble
import Definitions.Def_PartialMonitoringGame

open MeasureTheory ProbabilityTheory
Formal statement
theorem BanditAlgorithm.pmMinimaxRegret_eq_zero_of_universally_optimal
    {k d : ℕ} {𝕊 : Type*}
    [Fintype 𝕊] [MeasurableSpace 𝕊] [MeasurableSingletonClass 𝕊]
    (G : PartialMonitoringGame k d 𝕊) (a : Fin k)
    (ha : ∀ b : Fin k, ∀ i : Fin d, G.L a i ≤ G.L b i) :
    ∀ n : ℕ, pmMinimaxRegret G n = 0 := by
  sorry
Source
Lattimore and Szepesvári, Bandit Algorithms (2020), Section 37.8, Theorem 37.22, printed p. 503 (PDF p. 511), constant-policy conclusion; https://tor-lattimore.com/downloads/book/book.pdf

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