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Exact value decomposition forces separability

Disproved
MarkovEntanglement.value_decomposition_implies_separable

by tianyipeng · Aug 7, 2026 · Mathlib c5ea003 (Lean v4.30.0)

multi-agent-systemsreinforcement-learningseparabilityvalue-decomposition

Statement

Theorem. Consider a multi-agent Markov system with joint transition PπP^\piPπ and discount factor γ>0\gamma > 0γ>0. Suppose that for every choice of local reward functions r1,…,rNr_1,\dots,r_Nr1​,…,rN​ the resulting QQQ-function admits an exact decomposition Qπ=∑i=1NQi(si,ai)Q^\pi = \sum_{i=1}^N Q_i(s_i,a_i)Qπ=∑i=1N​Qi​(si​,ai​). Then the agents are separable, i.e.

Pπ  =  ∑j=1Kxj P1(j)⊗⋯⊗PN(j),∑jxj=1.P^\pi \;=\; \sum_{j=1}^{K} x_j\, P^{(j)}_1 \otimes \cdots \otimes P^{(j)}_N ,\qquad \textstyle\sum_j x_j = 1 .Pπ=j=1∑K​xj​P1(j)​⊗⋯⊗PN(j)​,∑j​xj​=1.

Notes

The converse of the previous result, and the half that makes the characterisation sharp. Together they say: value decomposition is exact if and only if the transition matrix is separable — Markov entanglement is not merely sufficient for decomposition error, it is necessary.

The quantifier over rewards is essential and easy to miss. A single reward vector can decompose by accident; the theorem requires decomposition for every reward, which is what pins down the transition structure rather than a coincidence of one value function.

Search terms: necessary and sufficient condition for value decomposition, additive Q-function, separable transition kernel, multi-agent reinforcement learning theory.

Preamble
import Mathlib
import Definitions.Def_markov_entanglement_multi

open scoped BigOperators
open MarkovEntanglement
Formal statement
namespace MarkovEntanglement

theorem value_decomposition_implies_separable
    {N : ℕ} {S : Fin N → Type*} [∀ i, Fintype (S i)] [∀ i, DecidableEq (S i)]
    (P : Matrix (Joint S) (Joint S) ℝ) (hP : IsTransitionMatrix P)
    (γ : ℝ) (hγ : 0 < γ) (hγ1 : γ < 1)
    (hdec : ∀ r : ∀ i, S i → ℝ, ∀ Q : Joint S → ℝ,
      IsBellmanQ P (fun p => ∑ i, r i (p i)) γ Q →
        ∃ Qi : ∀ i, S i → ℝ, IsValueDecomposition Q Qi) :
    IsSeparableN P := by
  sorry

end MarkovEntanglement
Source
Shuze Chen and Tianyi Peng, *Multi-agent Markov Entanglement*, arXiv:2506.02385v3, Theorem 2, p. 12
Read-back

What the Lean code literally says, in plain math · claude-opus-5

Read-back: value_decomposition_implies_separable

What the statement asserts. Fix a natural number NNN (implicit; N=0N = 0N=0 is allowed) and a family of types S0,…,SN−1S_0, \dots, S_{N-1}S0​,…,SN−1​ indexed by i∈{0,1,…,N−1}i \in \{0, 1, \dots, N-1\}i∈{0,1,…,N−1} (implicit), each assumed finite and equipped with decidable equality. Write

S  =  ∏iSi\mathcal S \;=\; \prod_{i} S_iS=i∏​Si​

for the joint space, whose elements are tuples ppp with pi∈Sip_i \in S_ipi​∈Si​ for each index iii. The statement says: for every real matrix PPP indexed by S×S\mathcal S \times \mathcal SS×S and every real number γ\gammaγ, if

  1. PPP is row-stochastic: Pp,q≥0P_{p,q} \ge 0Pp,q​≥0 for all p,q∈Sp,q \in \mathcal Sp,q∈S and ∑q∈SPp,q=1\sum_{q \in \mathcal S} P_{p,q} = 1∑q∈S​Pp,q​=1 for every p∈Sp \in \mathcal Sp∈S;
  2. 0<γ0 < \gamma0<γ (strict);
  3. γ<1\gamma < 1γ<1; and
  4. the decomposition hypothesis stated in full below holds,

then PPP is separable, meaning: there exist a natural number KKK, real coefficients x0,…,xK−1x_0, \dots, x_{K-1}x0​,…,xK−1​, and matrices Pi(k)P^{(k)}_iPi(k)​ indexed by Si×SiS_i \times S_iSi​×Si​ (one for each k<Kk < Kk<K and each index iii) such that every Pi(k)P^{(k)}_iPi(k)​ is row-stochastic, ∑k<Kxk=1\sum_{k<K} x_k = 1∑k<K​xk​=1, and

Pp,q  =  ∑k<Kxk∏iPi(k)(pi,qi)for all p,q∈S.P_{p,q} \;=\; \sum_{k<K} x_k \prod_{i} P^{(k)}_i(p_i, q_i) \qquad \text{for all } p, q \in \mathcal S .Pp,q​=k<K∑​xk​i∏​Pi(k)​(pi​,qi​)for all p,q∈S.

The coefficients xkx_kxk​ are not required to be nonnegative (an affine, not necessarily convex, combination), the equality is exact rather than approximate, and K=0K = 0K=0 is impossible because the coefficients must sum to 111.

The decomposition hypothesis, with its exact quantifier nesting. Hypothesis 4 is, verbatim in structure:

∀ r=(ri)i (ri:Si→R),∀ Q:S→R,[ ∀p∈S, Q(p)=∑iri(pi)+γ∑q∈SPp,qQ(q)⏟antecedent ⟹ ∃ (Qi)i (Qi:Si→R), ∀p∈S, Q(p)=∑iQi(pi) ].\forall\, r = (r_i)_i \ \big(r_i : S_i \to \mathbb R\big),\quad \forall\, Q : \mathcal S \to \mathbb R,\quad \Big[\ \underbrace{\forall p \in \mathcal S,\ Q(p) = \sum_i r_i(p_i) + \gamma \sum_{q \in \mathcal S} P_{p,q} Q(q)}_{\text{antecedent}} \ \Longrightarrow\ \exists\, (Q_i)_i \ \big(Q_i : S_i \to \mathbb R\big),\ \forall p \in \mathcal S,\ Q(p) = \sum_i Q_i(p_i) \ \Big].∀r=(ri​)i​ (ri​:Si​→R),∀Q:S→R,[ antecedent∀p∈S, Q(p)=i∑​ri​(pi​)+γq∈S∑​Pp,q​Q(q)​​ ⟹ ∃(Qi​)i​ (Qi​:Si​→R), ∀p∈S, Q(p)=i∑​Qi​(pi​) ].

The nesting is: two universal quantifiers on the outside — first over the family of per-index reward functions rrr, then over the function QQQ on the joint space — then a single implication whose antecedent is the Bellman fixed-point equation for PPP, discount γ\gammaγ, and reward p↦∑iri(pi)p \mapsto \sum_i r_i(p_i)p↦∑i​ri​(pi​), and whose consequent is an innermost existential over a family (Qi)i(Q_i)_i(Qi​)i​ followed by a universal over joint states ppp. Because the existential sits inside both universals, the witnessing family (Qi)i(Q_i)_i(Qi​)i​ is allowed to depend on both rrr and QQQ; no uniform, linear, or otherwise structured dependence on rrr is demanded, and no uniqueness of (Qi)i(Q_i)_i(Qi​)i​ is demanded.

What that hypothesis rules in and rules out.

  • It ranges only over rewards of the form p↦∑iri(pi)p \mapsto \sum_i r_i(p_i)p↦∑i​ri​(pi​), built from a family of per-index functions. Joint reward functions on S\mathcal SS that are not of this additive form are never quantified over, so the hypothesis says nothing about their value functions.
  • It ranges over all functions Q:S→RQ : \mathcal S \to \mathbb RQ:S→R, but each such QQQ is filtered by the antecedent. Since PPP is row-stochastic and 0<γ<10 < \gamma < 10<γ<1, the map Q↦(p↦∑iri(pi)+γ∑qPp,qQ(q))Q \mapsto \big(p \mapsto \sum_i r_i(p_i) + \gamma \sum_q P_{p,q} Q(q)\big)Q↦(p↦∑i​ri​(pi​)+γ∑q​Pp,q​Q(q)) is a γ\gammaγ-contraction in the supremum norm on the finite-dimensional space RS\mathbb R^{\mathcal S}RS; hence for each rrr there is exactly one QQQ satisfying the antecedent, and the hypothesis is neither vacuous nor over-broad: it asserts precisely that for every additive reward family, the unique discounted Bellman fixed point of PPP is a pointwise sum of per-index functions of the corresponding coordinate.
  • It imposes no relation between the witnessing (Qi)i(Q_i)_i(Qi​)i​ and the given (ri)i(r_i)_i(ri​)i​, no local Bellman equation on any SiS_iSi​, and no local transition matrix; it only requires the pointwise additive identity Q(p)=∑iQi(pi)Q(p) = \sum_i Q_i(p_i)Q(p)=∑i​Qi​(pi​).
  • The same γ\gammaγ constrained by hypotheses 2 and 3 appears inside the Bellman equation; the hypothesis makes no claim for any other discount factor.
  • Neither γ\gammaγ, nor rrr, nor QQQ appears in the conclusion.

Binders and what is constrained. NNN and the family SSS are implicit universally quantified parameters, with finiteness and decidable equality as typeclass hypotheses on each SiS_iSi​. PPP and γ\gammaγ are explicit universally quantified parameters, and the four hypotheses above are named premises. In the conclusion, the only variable inherited from the theorem's binders is PPP, which is constrained by hypotheses 1 and 4; the data KKK, (xk)k(x_k)_k(xk​)k​, and (Pi(k))k,i(P^{(k)}_i)_{k,i}(Pi(k)​)k,i​ appearing in the conclusion are all existentially quantified inside it. No variable in the conclusion is free or unconstrained.

Joint satisfiability. The hypotheses are simultaneously satisfiable, so the statement is not vacuously true by unsatisfiable premises. For instance, take N=1N = 1N=1: then S\mathcal SS is in bijection with S0S_0S0​ via p↦p0p \mapsto p_0p↦p0​, every function Q:S→RQ : \mathcal S \to \mathbb RQ:S→R is of the form p↦Q0(p0)p \mapsto Q_0(p_0)p↦Q0​(p0​), so hypothesis 4 holds for any row-stochastic PPP and any γ\gammaγ; choosing any γ∈(0,1)\gamma \in (0,1)γ∈(0,1) satisfies hypotheses 2 and 3. (In that instance the conclusion also holds, with K=1K = 1K=1 and x0=1x_0 = 1x0​=1.)

Degenerate cases the quantifiers include.

  • N=0N = 0N=0: the index set is empty, so S\mathcal SS is the empty product, a one-element set; every sum over indices is 000 and every product over indices is 111. Then PPP is forced by hypothesis 1 to be the 1×11\times11×1 matrix [1][1][1], hypothesis 4 asks only that the constant function QQQ solving Q=0+γQQ = 0 + \gamma QQ=0+γQ equal the empty sum, and the conclusion holds with K=1K = 1K=1, x0=1x_0 = 1x0​=1 (the empty tensor product being the all-ones matrix).
  • If some SiS_iSi​ is empty — permitted, since finiteness does not imply nonemptiness — then S\mathcal SS is empty, PPP is the empty matrix, hypothesis 1 holds vacuously, hypothesis 4 holds vacuously (all equations quantify over p∈Sp \in \mathcal Sp∈S), and the conclusion also holds trivially: matrix equality over an empty index type is automatic, and a row-stochastic Pi(k)P^{(k)}_iPi(k)​ exists for each iii (vacuously when SiS_iSi​ is empty, e.g. as a point mass when SiS_iSi​ is nonempty).
  • The strictness in 0<γ0 < \gamma0<γ excludes γ=0\gamma = 0γ=0 but the statement is otherwise silent about the boundary γ=1\gamma = 1γ=1, which is excluded by hypothesis 3.

Proof status. The proof body of the declaration is a placeholder; nothing is derived.

Human review
  • Endorsed by Shuze Chen · Aug 7, 2026

  • Endorsed by tianyipeng · Aug 7, 2026

    Confirmed by the mission captain (proposal self-audit).

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