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JAO Lemma 13: change of measure for the number of plays of the planted action

Proved
BanditAlgorithm.jao_two_state_planted_plays_change_of_measure

by Grace · Aug 5, 2026 · Mathlib c5ea003 (Lean v4.30.0)

markov-decision-processesreinforcement-learning

Throughout, the MDP is the collapsed two-state gadget of JAO Figure 3, presented by its defining equations rather than through an auxiliary definition: state 000 is s∘s_\circs∘​ with reward 000, state 111 is sps_psp​ with reward 111, the return probability is p(s∘∣sp,b)=δp(s_\circ \mid s_p, b) = \deltap(s∘​∣sp​,b)=δ for every action bbb, and the escape probability is p(sp∣s∘,b)=δp(s_p \mid s_\circ, b) = \deltap(sp​∣s∘​,b)=δ for every action other than the planted action aaa, for which it is δ+ε\delta + \varepsilonδ+ε. The gadget has diameter D′=1/δD' = 1/\deltaD′=1/δ. The reference MDP M0M_0M0​ is the same gadget with no planting.

Statement. For 0<ε≤δ≤130 < \varepsilon \le \delta \le \tfrac130<ε≤δ≤31​, every horizon TTT and every policy π\piπ,

Ea[N∘∗]  ≤  Eunif[N∘∗]+T2⋅εδ⋅2 Eunif[N∘∗],\mathbb{E}_a[N_\circ^*] \;\le\; \mathbb{E}_{\mathrm{unif}}[N_\circ^*] + \frac{T}{2}\cdot\frac{\varepsilon}{\sqrt{\delta}}\cdot\sqrt{2\,\mathbb{E}_{\mathrm{unif}}[N_\circ^*]},Ea​[N∘∗​]≤Eunif​[N∘∗​]+2T​⋅δ​ε​⋅2Eunif​[N∘∗​]​,

where N∘∗N_\circ^*N∘∗​ counts the rounds in which the planted action aaa is played in state s∘s_\circs∘​.

This is equation (37) of JAO (p. 1584), obtained from their Lemma 13 applied to f=N∘∗f = N_\circ^*f=N∘∗​, a function of the observed state-action history taking values in [0,T][0, T][0,T]. Lemma 13 itself states that for fff with values in [0,B][0, B][0,B], 0≤δ≤120 \le \delta \le \tfrac120≤δ≤21​ and 0≤ε≤1−2δ0 \le \varepsilon \le 1 - 2\delta0≤ε≤1−2δ,

Ea[f]  ≤  Eunif[f]+B2⋅εδ2 Eunif[N∘∗];\mathbb{E}_a[f] \;\le\; \mathbb{E}_{\mathrm{unif}}[f] + \frac{B}{2}\cdot\frac{\varepsilon}{\sqrt{\delta}}\sqrt{2\,\mathbb{E}_{\mathrm{unif}}[N_\circ^*]};Ea​[f]≤Eunif​[f]+2B​⋅δ​ε​2Eunif​[N∘∗​]​;

the hypothesis ε≤δ≤13\varepsilon \le \delta \le \tfrac13ε≤δ≤31​ of the present statement gives ε≤13≤1−2δ\varepsilon \le \tfrac13 \le 1 - 2\deltaε≤31​≤1−2δ, so the lemma applies.

The proof is a divergence decomposition followed by Pinsker's inequality. The planted and reference gadgets differ in exactly one row of the transition function, namely (s∘,a)(s_\circ, a)(s∘​,a), so the relative entropy between the two trajectory laws under a common policy is Eunif[N∘∗]\mathbb{E}_{\mathrm{unif}}[N_\circ^*]Eunif​[N∘∗​] times the relative entropy kl(δ+ε ∥ δ)≤ε2δ(1−δ)\mathrm{kl}(\delta + \varepsilon \,\|\, \delta) \le \frac{\varepsilon^2}{\delta(1-\delta)}kl(δ+ε∥δ)≤δ(1−δ)ε2​ of the two rows; Pinsker's inequality then converts this into a bound on the difference of expectations of any [0,B][0,B][0,B]-valued function. JAO note that the observation in an MDP is the next state rather than the reward, which is harmless here because the reward is a deterministic function of the state, so N∘∗N_\circ^*N∘∗​ is a function of the state-action sequence and the argument of Auer et al. goes through verbatim.

Preamble
import Mathlib.Data.Real.Sqrt
import Mathlib.Analysis.SpecialFunctions.Log.Basic
import Definitions.Def_UCRL2ConfidenceSets

open MeasureTheory ProbabilityTheory
Formal statement
theorem BanditAlgorithm.jao_two_state_planted_plays_change_of_measure
    {m : ℕ} (δ ε : ℝ) (hδ0 : 0 < δ) (hδ : δ ≤ 1 / 3) (hε0 : 0 < ε) (hεδ : ε ≤ δ)
    (a : Fin m) (M M₀ : FiniteMDP 2 m)
    (hr0 : ∀ b, M.r 0 b = 0) (hr1 : ∀ b, M.r 1 b = 1)
    (hP1 : ∀ b, (M.P 1 b 0 : ℝ) = δ)
    (hP0 : ∀ b, (M.P 0 b 1 : ℝ) = δ + (if b = a then ε else 0))
    (hr0' : ∀ b, M₀.r 0 b = 0) (hr1' : ∀ b, M₀.r 1 b = 1)
    (hP1' : ∀ b, (M₀.P 1 b 0 : ℝ) = δ) (hP0' : ∀ b, (M₀.P 0 b 1 : ℝ) = δ)
    (T : ℕ) (π : MDPPolicy 2 m) :
    (∫ h, (mdpVisitCount h T 0 a : ℝ) ∂(mdpMeasure M (mdpStateDirac 0) π T))
      ≤ (∫ h, (mdpVisitCount h T 0 a : ℝ) ∂(mdpMeasure M₀ (mdpStateDirac 0) π T))
        + (T : ℝ) / 2 * (ε / Real.sqrt δ)
            * Real.sqrt
                (2 * ∫ h, (mdpVisitCount h T 0 a : ℝ)
                        ∂(mdpMeasure M₀ (mdpStateDirac 0) π T)) := by
  sorry
Source
Jaksch, Ortner & Auer, "Near-optimal Regret Bounds for Reinforcement Learning", JMLR 11 (2010) 1563-1600, Section 6 (pp. 1583-1586): equations (34)-(37), Lemma 13 and the concluding computation. Lemma 13 is adapted from Auer, Cesa-Bianchi, Freund & Schapire, "The Nonstochastic Multiarmed Bandit Problem", SIAM J. Comput. 32 (2002) 48-77, Theorem A.2 and its proof in the appendix.

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