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Finite-horizon policy ratios are trajectory Radon-Nikodym derivatives
Statement
Assume AC and the finite-horizon dataset conditions of the preceding definition, with full horizon coverage . For , let be the finite set of trajectory prefixes through , and let be the marginal law of this prefix under policy . On a behavior-positive prefix define
and set on behavior-null prefixes. Then , and is a version of up to -almost-everywhere equality. In particular, for every real integrable under , the full-horizon weight satisfies
The same identity holds prefixwise: for each and every real integrable under , with evaluated on the observed prefix,
Statement references
Absolute continuity transfers null sets from the behavior measure to the target measure.
The Radon-Nikodym derivative is the almost-everywhere class of densities.
Radon-Nikodym derivatives satisfy the chain rule along nu << mu << lambda: For sigma-finite measures on a common finite exhaustion, the derivative of factors as the product of the two derivatives.
A real function is integrable exactly when its absolute value has finite integral.
The Lebesgue integral is linear on integrable real and complex functions.
Sutton and Barto, Reinforcement Learning: An Introduction, 2nd ed.
Jiang and Li, Doubly Robust Off-policy Value Evaluation for Reinforcement Learning
Facts & Assumptions
Given: AC, a finite MDP with fixed initial state , finite horizon , stationary policies , their induced laws , full horizon coverage , and integrable .
The Axiom of Choice: AC states that every family of nonempty sets has a choice function; the finite-MDP path-law supplier and the RN density and chain-rule suppliers assume it.
Finite-horizon offline reinforcement-learning dataset and coverage: for each policy , a feasible length- trajectory atom has mass ; the initial state and transition/reward law are the same for and .
A finite MDP policy generates action-state-reward trajectories: under AC, a finite MDP and stationary policy have the finite-prefix probabilities given by the same product formula.
Absolute continuity of a signed or complex measure with respect to a positive measure: means every -null measurable set is -null.
A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density: under AC, a signed measure with sigma-finite common finite exhaustion has a measurable density unique up to -almost-everywhere equality; if is finite, the density is integrable.
The Radon-Nikodym derivative as an almost-everywhere equivalence class: is the almost-everywhere class of functions representing by measurable-set integrals.
Radon-Nikodym derivatives satisfy the chain rule along nu << mu << lambda: under AC and the common finite-exhaustion hypotheses, implies -almost everywhere.
Integrating against a density agrees with integrating the product: for nonnegative measurable , on any measure space.
Integrable real and complex functions, and their integrals and The Lebesgue integral is linear on : real integrable functions have finite positive and negative integrals, and their integrals are linear.
Proof
Given: The hypotheses and conventions in the Statement.
For a complete trajectory atom , [F1] gives . If this atom has positive target mass, coverage makes its behavior mass positive. The initial state and every transition/reward factor are common, so on every behavior-positive atom the quotient is exactly ; if the target mass is zero, this quotient is zero.
Fix and project the full trajectory law onto . If a prefix set is behavior-null, its inverse image in is behavior-null; full coverage makes that inverse image target-null, so . The prefix space is finite. Let be counting measure on it; , and the whole prefix space is a common finite exhaustion for , , and . By [A1], [F4]–[F6] give the RN density classes and their chain rule. On each behavior-positive prefix, [F1]–[F2] give the atom-mass quotient by cancelling the common initial and transition/reward factors, leaving . Thus almost everywhere.
Fix and a real integrable under . Step 2.1 supplies the prefix density . Applying [F7] to and gives and equality of the two weighted integrals for each part. Subtracting the finite positive and negative integrals by [F8] proves the prefix expectation identity. Taking and gives the full-horizon assertion.
Depends on
- Absolute continuity of a signed or complex measure with respect to a positive measure
- The Axiom of Choice
- Integrable real and complex functions, and their integrals
- Finite-horizon offline reinforcement-learning dataset and coverage
- The Radon-Nikodym derivative as an almost-everywhere equivalence class
- A finite MDP policy generates action-state-reward trajectories
- Radon-Nikodym derivatives satisfy the chain rule along nu << mu << lambda
- Integrating against a density agrees with integrating the product
- The Lebesgue integral is linear on $L^1(\mu)$
- A sigma-finite signed measure that is absolutely continuous with respect to a sigma-finite positive measure has a unique almost-everywhere density
Used by
- Finite-horizon doubly robust off-policy value evaluation Algorithm
- Ordinary, per-decision, and weighted trajectory importance sampling Algorithm
- Reasoning-training evidence and evaluation targets Remark
- Doubly robust evaluation is unbiased under either exact component Theorem
- Trajectory and per-decision importance sampling are unbiased under coverage Theorem
Dependency tree · two levels
45 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)
- Nan Jiang and Lihong Li, Doubly Robust Off-policy Value Evaluation for Reinforcement Learning (standard reference, not scraped)