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.
Performance-difference identity for an arbitrary bounded baseline
Statement
Let a finite discounted CMDP, a bounded transition signal , a bounded function , and stationary policies be given. All sums below over range over , respectively. For a policy define
Put
Then the exact performance-difference identity is
Statement references
Finite CMDP data and finite-time state masses.
Expected discounted-cost feasibility constraint: The expected discounted transition-signal sum .
Normalized discounted state occupancy: The normalized discounted state occupancy .
Probability mass and boundedness facts used to justify the discounted series.
Achiam et al., Constrained Policy Optimization, supplementary material, Appendix 10.1.
Facts & Assumptions
Given: A finite discounted CMDP, bounded and , stationary policies , and .
Finite discounted constrained Markov decision process: the finite-time state masses obey the one-step transition recurrence with joint masses .
Expected discounted-cost feasibility constraint: is the discounted series of finite sums of against the time- transition masses.
Normalized discounted occupancy is a probability distribution: every is a probability distribution, and every bounded transition-signal discounted series is absolutely convergent.
Proof
Fix a policy . For each , let be the finite transition-mass sum of at time , and let . By [F1] and the definition of , the finite sum of at time is . Summing after multiplication by for telescopes to .
The remainder satisfies by [F4] and [F5]. Also , so [F4] makes its discounted series convergent. Passing to the limit in step 1.1 and using [F2]–[F3] gives .
Apply the identity in step 2.1 to and and subtract; the initial terms cancel. Add and subtract . The first resulting term is by its definition, and the second is . Dividing by gives the claimed identity.
Depends on
- Finite discounted constrained Markov decision process
- Expected discounted-cost feasibility constraint
- Normalized discounted state occupancy
- For $|r| < 1$ the sequence $r^k$ is null, and for $|r| > 1$ the sequence $|r|^k$ diverges to $+\infty$
- Normalized discounted occupancy is a probability distribution
Used by
Dependency tree · two levels
27 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
- Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel, Constrained Policy Optimization, supplementary material (standard reference, not scraped)