A universally optimal action has zero partial-monitoring minimax regret
ProvedBanditAlgorithm.pmMinimaxRegret_eq_zero_of_universally_optimalbanditsminimax-regretpartial-monitoring
Let be a finite partial-monitoring game. Suppose there is an action whose loss is no larger than the loss of any action on every outcome : . Then, for every horizon ,
Indeed, always playing achieves zero worst-case regret, while comparison with 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
sorrySource
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