Eq. (A 2) — the return increments are Cesàro-convergent to
ProvedJewellMRP.InfiniteStep.return_increment_cesaroaverage-rewardcesaro-meanmarkov-renewalp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1
Let be an irreducible row-stochastic matrix on a nonempty finite state set with stationary probability vector , let be the matrix whose rows all equal , and let be the -step return of the fixed policy for one-step rewards and arbitrary terminal rewards . Then
So the long-run per-transition increase of the expected return is the system gain , the same from every starting state.
Formalization Note The paper writes . For a periodic chain (such as the paper's own example) the increments oscillate and only their Cesàro means converge, so the limit is stated in the Cesàro sense. The statement holds for every terminal-reward vector .
Preamble
import Mathlib import Definitions.Def_JewellMRP_InfiniteStep_Model open Matrix Filter Topology
Formal statement
namespace JewellMRP.InfiniteStep
theorem return_increment_cesaro {S : Type*} [Fintype S] [DecidableEq S] [Nonempty S]
(P : Matrix S S ℝ) (hP : IsErgodic P) (π : S → ℝ) (hπ : IsStationary P π)
(ρ V0 : S → ℝ) :
Tendsto (cesaroMean (fun m => stepReturn P ρ V0 m - stepReturn P ρ V0 (m - 1))) atTop
(𝓝 (limitMatrix π *ᵥ ρ)) ∧
limitMatrix π *ᵥ ρ = fun _ => gain π ρ := by sorry
end JewellMRP.InfiniteStep
Source
Jewell, Markov-Renewal Programming. II: Infinite Return Models, Example, Oper. Res. 11 (1963), p. 966, Eq. (A 2)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.