How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
A policy score has zero conditional mean before the action
Statement
Assume the Axiom of Choice. Let be a nonempty finite state set, let each be a finite nonempty action set, and let be a fixed normalized joint law for the next state and reward. Fix an initial state and a terminal set . 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 , let be the information available just before the time- action, and let when , where is the first terminal time. Define when . Then, coordinate by coordinate,
Statement references
Facts & Assumptions
Given: The stated finite MDP, initial state, terminal set, differentiable policy family, Axiom of Choice, and its action-state-reward path law.
The Axiom of Choice: every family of nonempty sets has a choice function; the path-law construction below is the use of this axiom.
Finite Markov decision process with joint reward-transition law: the state, reward, and state-dependent action sets are finite; for each admissible the joint masses are nonnegative and sum to one; a stationary policy is a normalized action distribution.
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 .
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.
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.
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.
The sum over a finite index set, and its product form: a finite sum is independent of the enumeration used to compute it.
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.
Fix and a positive-probability atom of contained in , and let be its current state. By [F3], adjoining action and next outcome multiplies the mass of by . Summing over the finite next-state and reward sets and using [F1] gives . On atoms contained in , by definition; on zero-probability atoms no conditional value is prescribed.
Enumerate the finite support as . 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 ending at , the conditional score mean is . The first equality uses positivity on the support and the derivative of the logarithm; the second uses [F5], [F6], and the normalization in [F2].
To verify that this mean is a conditional-expectation version, note that has finitely many atoms because its state, action, and reward alphabets are finite. The score coordinates are bounded at fixed , since there are finitely many state-action pairs. On each positive-probability atom of , define to have the constant value ; set on every null atom. If , step 1.1 and the displayed calculation show that this value is zero; if , on by definition, so the value is again zero. Thus is the zero, integrable, -measurable vector. Every event in is a union of finitely many atoms, and summing the atom integral identities gives for every , including null atoms. This verifies [F4], so is a version of and is zero almost surely.
Depends on
- Finite Markov decision process with joint reward-transition law
- Differentiable stationary stochastic policies and score vectors
- A finite MDP policy generates action-state-reward trajectories
- The Axiom of Choice
- Conditional expectation given a sigma algebra
- The sum $\sum_{i \in S} a_i$ over a finite index set, and its product form
- Sums and scalar multiples of totally differentiable maps are totally differentiable with the expected derivatives
- The principle of mathematical induction
Used by
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Sources
- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, second-edition draft (standard reference, not scraped)
- Ronald J. Williams, Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Machine Learning 8 (1992) (standard reference, not scraped)