AlgorithmAlgorithm: Literature-sourcedProof: Not applicablePipeline-generatedjudge pass (gpt-6-luna)

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.
AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.

These labels describe origin, not correctness: citations and verification chips remain separate evidence.

Exact value iteration for the Bellman optimality operator

Definition

Input a finite MDP, a discount $0 \leq γ < 1$ , an initial value function $V_{0} \in R^{S}$ , and an integer $m \geq 0$ . Set $V_{j + 1} = T^{*} V_{j}$ for $j = 0, \dots, m - 1$ and return $V_{m}$ . This procedure returns the exact $m$ th iterate and makes no finite-termination claim for convergence.

Definition references

The Bellman optimality map used at each step.

Sutton and Barto, Reinforcement Learning: An Introduction.

Kaelbling, Littman, and Cassandra, Brown Technical Report CS-96-08.

Depends on

Used by

Dependency tree · two levels

13 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.

Sources

Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, 2nd ed. (standard reference, not scraped)
Leslie Pack Kaelbling, Michael L. Littman, and Anthony R. Cassandra, Planning and Acting in Partially Observable Stochastic Domains, Brown CS-96-08 (standard reference, not scraped)