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The UCRL2 doubling rule generates a valid phase schedule

Proved
BanditAlgorithm.mdp_ucrl2_phase_schedule_from_doubling_rule

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

markov-decision-processesreinforcement-learning

Fix S≥1S\ge1S≥1 states, A≥1A\ge1A≥1 actions, a horizon n≥1n\ge1n≥1 and a trajectory hhh of nnn rounds. The phase-start map of UCRL2 -- a new phase begins at time u+1u+1u+1 as soon as the number of visits to some pair since the start of the current phase has reached max⁡(1,N)\max(1,N)max(1,N), where NNN is the number of visits accumulated to that pair before the phase began -- cuts {0,…,n−1}\{0,\dots,n-1\}{0,…,n−1} into finitely many consecutive nonempty phases: there are a number KKK of phases and a nondecreasing sequence of times τ0=0<τ1<⋯<τK=n\tau_0=0<\tau_1<\dots<\tau_K=nτ0​=0<τ1​<⋯<τK​=n, stationary at nnn beyond KKK, such that

  • every time uuu in [τk,τk+1)[\tau_k,\tau_{k+1})[τk​,τk+1​) has τk\tau_kτk​ as the start of its phase;
  • the doubling inequality holds: for k<Kk<Kk<K and every pair (s,a)(s,a)(s,a), the visits to (s,a)(s,a)(s,a) before τk+1\tau_{k+1}τk+1​ number at most Nk(s,a)+max⁡(1,Nk(s,a))N_k(s,a)+\max(1,N_k(s,a))Nk​(s,a)+max(1,Nk​(s,a)), where Nk(s,a)N_k(s,a)Nk​(s,a) counts the visits before τk\tau_kτk​ -- no pair is visited during a phase more often than it had been visited before it;
  • K≤3SAnK\le3\sqrt{SAn}K≤3SAn​.

The last bound is the combinatorial heart of the algorithm: each of the SASASA pairs can trigger the end of a phase only after its count has doubled, so it triggers at most log⁡2\log_2log2​ of its final count many phases, and Jensen's inequality over the pairs turns the sum of those logarithms into SAn\sqrt{SAn}SAn​.

Formalization Note The doubling inequality is stated with an addition rather than a truncated subtraction, which is the same statement for natural numbers and avoids the cutoff.

Preamble
import Definitions.Def_UCRL2Algorithm

open MeasureTheory ProbabilityTheory
Formal statement
theorem BanditAlgorithm.mdp_ucrl2_phase_schedule_from_doubling_rule
    (S A n : ℕ) (hS : 0 < S) (hA : 0 < A) (hn : 0 < n)
    (h : MDPTrajectory S A n) :
    ∃ (K : ℕ) (τ : ℕ → ℕ),
      τ 0 = 0 ∧ τ K = n ∧ (∀ k, τ k ≤ τ (k + 1)) ∧
      (∀ k < K, τ k < τ (k + 1)) ∧
      (∀ k, K ≤ k → τ k = n) ∧
      (K : ℝ) ≤ 3 * Real.sqrt (S * A * n) ∧
      (∀ k < K, ∀ u ∈ Finset.Ico (τ k) (τ (k + 1)), mdpPhaseStart h u = τ k) ∧
      (∀ k < K, ∀ (s : Fin S) (a : Fin A),
        mdpVisitCount h (τ (k + 1)) s a
          ≤ mdpVisitCount h (τ k) s a + max 1 (mdpVisitCount h (τ k) s a)) := by
  sorry
Source
Lattimore and Szepesvari, Bandit Algorithms (CUP 2020), Section 38.5 and the phase-counting step of Section 38.6, printed pp. 524-527 / PDF pp. 533-536; Jaksch, Ortner and Auer, Near-optimal regret bounds for reinforcement learning, JMLR 11 (2010), Section 3 and Proposition 18.

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