JAO Lemma 13: change of measure for the number of plays of the planted action
ProvedBanditAlgorithm.jao_two_state_planted_plays_change_of_measureThroughout, the MDP is the collapsed two-state gadget of JAO Figure 3, presented by its defining equations rather than through an auxiliary definition: state is with reward , state is with reward , the return probability is for every action , and the escape probability is for every action other than the planted action , for which it is . The gadget has diameter . The reference MDP is the same gadget with no planting.
Statement. For , every horizon and every policy ,
where counts the rounds in which the planted action is played in state .
This is equation (37) of JAO (p. 1584), obtained from their Lemma 13 applied to , a function of the observed state-action history taking values in . Lemma 13 itself states that for with values in , and ,
the hypothesis of the present statement gives , 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 , so the relative entropy between the two trajectory laws under a common policy is times the relative entropy of the two rows; Pinsker's inequality then converts this into a bound on the difference of expectations of any -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 is a function of the state-action sequence and the argument of Auer et al. goes through verbatim.
import Mathlib.Data.Real.Sqrt import Mathlib.Analysis.SpecialFunctions.Log.Basic import Definitions.Def_UCRL2ConfidenceSets open MeasureTheory ProbabilityTheory
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