Alphabeta Math
LemmaStatement: Literature-sourcedProof: AI-generatedPipeline-generatedjudge pass (gpt-6-luna)
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A policy score has zero conditional mean before the action

Statement

Assume the Axiom of Choice. Let S be a nonempty finite state set, let each A(s) be a finite nonempty action set, and let p(s′,r∣s,a) be a fixed normalized joint law for the next state and reward. Fix an initial state and a terminal set S†⊆S. Under a differentiable stationary policy family with a support that is fixed and strictly positive on that support throughout its open parameter domain, assume the induced episode terminates almost surely. For t≥0, let Ht=σ(S0,A0,R1,…,At−1,Rt,St) be the information available just before the time-t action, and let ψt=∇θlog⁡πθ(At∣St) when t<T, where T is the first terminal time. Define ψt=0 when t≥T. Then, coordinate by coordinate,

Eθ[ψt∣Ht]=0almost surely for every t≥0.

Facts & Assumptions

Given: The stated finite MDP, initial state, terminal set, differentiable policy family, Axiom of Choice, and its action-state-reward path law.

[A1]

The Axiom of Choice: every family of nonempty sets has a choice function; the path-law construction below is the use of this axiom.

[F1]

Finite Markov decision process with joint reward-transition law: the state, reward, and state-dependent action sets are finite; for each admissible (s,a) the joint masses p(s′,r∣s,a) are nonnegative and sum to one; a stationary policy is a normalized action distribution.

[F2]

Differentiable stationary stochastic policies and score vectors: the policy probabilities are totally differentiable in the open parameter domain, and each state has a fixed support on which every probability is positive; on that support ψθ(s,a)=∇θlog⁡πθ(a∣s).

[F3]

A finite MDP policy generates action-state-reward trajectories: under Choice, each feasible finite trajectory prefix has probability equal to the product of its action probabilities and joint transition-reward masses.

[F4]

Conditional expectation given a sigma algebra: a conditional-expectation version is integrable, measurable with respect to the conditioning sigma-algebra, and has the same integral as the original variable on every event in that sigma-algebra.

[F5]

Sums and scalar multiples of totally differentiable maps are totally differentiable with the expected derivatives: the sum of two totally differentiable maps is totally differentiable with the sum of their derivatives.

[F6]

The sum ∑i∈Sai over a finite index set, and its product form: a finite sum is independent of the enumeration used to compute it.

[F7]

The principle of mathematical induction: a property proved at zero and preserved from each natural number to its successor holds for every natural number.

Proof

Given: The hypotheses and conventions in the Statement.

1.1givenA1F1F3algebra

Fix t and a positive-probability atom h of Ht contained in {T>t}, and let s be its current state. By [F3], adjoining action a and next outcome (s′,r) multiplies the mass of h by πθ(a∣s)p(s′,r∣s,a). Summing over the finite next-state and reward sets and using [F1] gives Pr⁡(At=a∣h)=πθ(a∣s). On atoms contained in {T≤t}, ψt=0 by definition; on zero-probability atoms no conditional value is prescribed.

2.1F2F5F6F7step 1.1algebra

Enumerate the finite support as B(s)={a0,…,am−1}. By induction on the number of terms, [F5] gives the derivative of this finite sum as the sum of the derivatives: the empty sum has derivative zero, and adjoining one term uses the binary sum rule. For each positive-probability history h⊆{T>t} ending at s, the conditional score mean is ∑a∈B(s)πθ(a∣s)∇θlog⁡πθ(a∣s)=∑a∈B(s)∇θπθ(a∣s)=∇θ1=0. The first equality uses positivity on the support and the derivative of the logarithm; the second uses [F5], [F6], and the normalization in [F2].

3.1F1F2F4step 1.1step 2.1algebra∎

To verify that this mean is a conditional-expectation version, note that Ht has finitely many atoms because its state, action, and reward alphabets are finite. The score coordinates are bounded at fixed θ, since there are finitely many state-action pairs. On each positive-probability atom h of Ht, define Yt to have the constant value Pθ(h)−1∫hψt dPθ; set Yt=0 on every null atom. If h⊆{T>t}, step 1.1 and the displayed calculation show that this value is zero; if h⊆{T≤t}, ψt=0 on h by definition, so the value is again zero. Thus Yt is the zero, integrable, Ht-measurable vector. Every event in Ht is a union of finitely many atoms, and summing the atom integral identities gives ∫EYt dPθ=∫Eψt dPθ for every E∈Ht, including null atoms. This verifies [F4], so Yt is a version of Eθ[ψt∣Ht] and is zero almost surely.

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