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Regret bound for any trajectory admitting an optimistic phase run

Proved
BanditAlgorithm.mdp_ucrl2_regret_bound_of_optimistic_phase_run

by Grace · Aug 2, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-decision-processesreinforcement-learning

There is a universal constant C>0C>0C>0 such that, for every MDP MMM with S≥2S\ge2S≥2 states, A≥1A\ge1A≥1 actions and diameter D(M)≥1D(M)\ge1D(M)≥1, every horizon n≥1n\ge1n≥1, every δ∈(0,1)\delta\in(0,1)δ∈(0,1) and every trajectory hhh of nnn rounds admitting an optimistic phase run, the regret satisfies

R^n(h)<C D(M) SAnlog⁡(nSA/δ).\widehat R_n(h) < C\, D(M)\, S\sqrt{An\log(nSA/\delta)}.Rn​(h)<CD(M)SAnlog(nSA/δ)​.

Call an optimistic phase run for a trajectory hhh of nnn rounds the following data: a partition of {0,…,n−1}\{0,\dots,n-1\}{0,…,n−1} into consecutive phases [τk,τk+1)[\tau_k,\tau_{k+1})[τk​,τk+1​), k<Kk<Kk<K, with τ0=0\tau_0=0τ0​=0 and τK=n\tau_K=nτK​=n, and for each phase a gain ρk\rho_kρk​, a bias vkv_kvk​ and a transition matrix qkq_kqk​, subject to

  1. few phases: K≤3SAnK\le 3\sqrt{SAn}K≤3SAn​;
  2. optimism: ρ∗≤ρk\rho^{*}\le\rho_kρ∗≤ρk​;
  3. span: vk(x)−vk(y)≤D(M)v_k(x)-v_k(y)\le D(M)vk​(x)−vk​(y)≤D(M) for all states x,yx,yx,y;
  4. Bellman equation along the trajectory: ρk+vk(St)=rAt(St)+⟨qk(St),vk⟩\rho_k+v_k(S_t)=r_{A_t}(S_t)+\langle q_k(S_t),v_k\rangleρk​+vk​(St​)=rAt​​(St​)+⟨qk​(St​),vk​⟩ for every round ttt of phase kkk;
  5. estimation error: ∑k<K∑t∈[τk,τk+1)⟨qk(St)−PAt(St),vk⟩≤D(M)(2+1)14Slog⁡(2SAn/δ)SAn\sum_{k<K}\sum_{t\in[\tau_k,\tau_{k+1})}\langle q_k(S_t)-P_{A_t}(S_t),v_k\rangle\le D(M)(\sqrt2+1)\sqrt{14S\log(2SAn/\delta)}\sqrt{SAn}∑k<K​∑t∈[τk​,τk+1​)​⟨qk​(St​)−PAt​​(St​),vk​⟩≤D(M)(2​+1)14Slog(2SAn/δ)​SAn​;
  6. martingale fluctuation: ∑k<K∑t∈[τk,τk+1)(⟨PAt(St),vk⟩−vk(St+1))≤D(M)2nlog⁡(2/δ)\sum_{k<K}\sum_{t\in[\tau_k,\tau_{k+1})}\bigl(\langle P_{A_t}(S_t),v_k\rangle-v_k(S_{t+1})\bigr)\le D(M)\sqrt{2n\log(2/\delta)}∑k<K​∑t∈[τk​,τk+1​)​(⟨PAt​​(St​),vk​⟩−vk​(St+1​))≤D(M)2nlog(2/δ)​.

This is the deterministic half of the analysis of UCRL2 (Lattimore--Szepesvari, Section 38.6): no probability appears, the whole argument being the per-phase telescoping of the Bellman equation, which turns the regret into the sum of the three quantities bounded in (3), (5) and (6), followed by the arithmetic that collects them into the stated bound.

Formalization Note The states and actions of the trajectory are named by functions on N\mathbb NN agreeing with hhh below nnn; this lets the terminal state SnS_nSn​, which the trajectory does not record but the telescoping and the martingale term refer to, be an unconstrained extra datum. The hypothesis S≥2S\ge2S≥2 is not a restriction: a one-state MDP has empty diameter supremum, hence D(M)=0D(M)=0D(M)=0.

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

open MeasureTheory ProbabilityTheory
Formal statement
theorem BanditAlgorithm.mdp_ucrl2_regret_bound_of_optimistic_phase_run :
    ∃ C : ℝ, 0 < C ∧
      ∀ S A n : ℕ, 2 ≤ S → 0 < A → 0 < n →
        ∀ δ : ℝ, δ ∈ Set.Ioo (0 : ℝ) 1 →
          ∀ M : FiniteMDP S A, 1 ≤ mdpDiameter M →
            ∀ (h : MDPTrajectory S A n) (st : ℕ → Fin S) (act : ℕ → Fin A),
              (∀ t : Fin n, h t = (st t, act t)) →
              ∀ (K : ℕ) (τ : ℕ → ℕ) (ρ : ℕ → ℝ) (v : ℕ → Fin S → ℝ)
                (q : ℕ → Fin S → Fin S → ℝ),
                τ 0 = 0 → τ K = n → (∀ k, τ k ≤ τ (k + 1)) →
                (K : ℝ) ≤ 3 * Real.sqrt (S * A * n) →
                (∀ k < K, mdpOptimalGain M ≤ ρ k) →
                (∀ k < K, ∀ x y : Fin S, v k x - v k y ≤ mdpDiameter M) →
                (∀ k < K, ∀ t ∈ Finset.Ico (τ k) (τ (k + 1)),
                  ρ k + v k (st t)
                    = M.r (st t) (act t) + ∑ s', q k (st t) s' * v k s') →
                (∑ k ∈ Finset.range K, ∑ t ∈ Finset.Ico (τ k) (τ (k + 1)),
                    ∑ s', (q k (st t) s' - (M.P (st t) (act t) s' : ℝ)) * v k s'
                  ≤ mdpDiameter M * (Real.sqrt 2 + 1) *
                      Real.sqrt (14 * S * Real.log (2 * S * A * n / δ)) *
                      Real.sqrt (S * A * n)) →
                (∑ k ∈ Finset.range K, ∑ t ∈ Finset.Ico (τ k) (τ (k + 1)),
                    ((∑ s', (M.P (st t) (act t) s' : ℝ) * v k s') - v k (st (t + 1)))
                  ≤ mdpDiameter M * Real.sqrt (2 * n * Real.log (2 / δ))) →
                mdpRegret M n h <
                  C * mdpDiameter M * S *
                    Real.sqrt (A * n * Real.log (n * S * A / δ)) := by
  sorry
Source
Lattimore and Szepesvari, Bandit Algorithms (CUP 2020), Section 38.6, Eq. (38.18)-(38.20) and the display collecting the three terms; after Jaksch, Ortner and Auer, Near-optimal regret bounds for reinforcement learning, JMLR 11 (2010), Section 4.

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