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MDP minimax regret lower bound E[R^n]≥CDSAn\mathbb{E}[\hat R_n] \ge C\sqrt{DSAn}E[R^n​]≥CDSAn​ (strengthened diameter hypothesis)

Proved
BanditAlgorithm.mdp_regret_lower_bound_large_diameter

by Shuze Chen · Aug 3, 2026 · Mathlib 0df444a (Lean v4.33.1)

(MDP minimax lower bound; L&S Theorem 38.7, with the diameter hypothesis strengthened) There is a universal constant C>0C > 0C>0 such that for all S≥3S \ge 3S≥3, A≥2A \ge 2A≥2, D≥20 (1+log⁡AS)D \ge 20\,(1 + \log_A S)D≥20(1+logA​S) and n≥DSAn \ge DSAn≥DSA: for any policy π\piπ there exists an MDP MMM with SSS states, AAA actions, rewards in [0,1][0,1][0,1] and diameter at most DDD (stated via the [0,∞][0,\infty][0,∞]-valued diameter, which forces MMM to be strongly connected) and an initial state distribution such that

E[R^n]≥CDSAn.\mathbb{E}[\hat R_n] \ge C\sqrt{DSAn}.E[R^n​]≥CDSAn​.

Why the hypothesis differs from the book. L&S state this with D≥6+2log⁡ASD \ge 6 + 2\log_A SD≥6+2logA​S, which appears too weak. In the §38.7 construction the diameter is realised by the pair (sg,sb)(s_g,s_b)(sg​,sb​) and equals 2(1/δ+d+1)2(1/\delta + d + 1)2(1/δ+d+1), so D(M)≤DD(M) \le DD(M)≤D forces 1/δ≤D/2−(d+1)1/\delta \le D/2 - (d+1)1/δ≤D/2−(d+1); a tree with Ω(S)\Omega(S)Ω(S) leaves needs depth d≳log⁡ASd \gtrsim \log_A Sd≳logA​S, so at the boundary d+1≈D/2d+1 \approx D/2d+1≈D/2 and the sojourn collapses to 1/δ=O(1)1/\delta = O(1)1/δ=O(1). The D\sqrt{D}D​ in DSAn\sqrt{DSAn}DSAn​ is the sojourn — each decision carries Θ(D)\Theta(D)Θ(D) rounds of reward consequence but returns one bit of feedback — so with 1/δ=O(1)1/\delta = O(1)1/δ=O(1) the construction yields only SAn/D\sqrt{SAn/D}SAn/D​. The original paper (Jaksch, Ortner & Auer, JMLR 11 (2010) 1563-1600, Thm 5) assumes D≥20log⁡ASD \ge 20\log_A SD≥20logA​S with S,A≥10S,A \ge 10S,A≥10. The form 20(1+log⁡AS)20(1 + \log_A S)20(1+logA​S) used here implies both D≥20D \ge 20D≥20 and D≥20log⁡ASD \ge 20\log_A SD≥20logA​S and keeps 1/δ≥0.4 D1/\delta \ge 0.4\,D1/δ≥0.4D uniformly, so no case split for small DDD is needed. The constant 202020 is not optimised: any ccc leaving Θ(D)\Theta(D)Θ(D) slack works.

Preamble
import Definitions.Def_FiniteMDPLearning
import Mathlib.Data.Real.Sqrt
import Mathlib.Analysis.SpecialFunctions.Log.Basic


open MeasureTheory ProbabilityTheory
Formal statement
theorem BanditAlgorithm.mdp_regret_lower_bound_large_diameter :
    ∃ C : ℝ, 0 < C ∧
      ∀ S A n : ℕ, ∀ D : ℝ, 3 ≤ S → 2 ≤ A →
        20 * (1 + Real.log S / Real.log A) ≤ D → D * S * A ≤ n →
        ∀ π : MDPPolicy S A,
          ∃ M : FiniteMDP S A, ∃ μ0 : MDPStateDistribution S,
            mdpDiameterENN M ≤ ENNReal.ofReal D ∧
            C * Real.sqrt (D * S * A * n) ≤
              ∫ h, mdpRegret M n h ∂(mdpMeasure M μ0 π n) := by
  sorry
Source
L&S Theorem 38.7, p.523, with the diameter hypothesis strengthened to D >= 20(1+log_A S) following Jaksch-Ortner-Auer, JMLR 11 (2010) 1563-1600, Thm 5

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