AWhat changed
Connected entropy-regularized inverse learning to occupancy-measure matching and trained policies against a discriminator of expert versus learner state-action samples.
Scientific scope
Connected entropy-regularized inverse learning to occupancy-measure matching and trained policies against a discriminator of expert versus learner state-action samples. Learner requires its own environment interactions; absence of expert queries or external rewards is not offline demonstration-only training. The finite-state/action duality analysis and ideal discriminator optimum do not certify nonlinear GAN/TRPO training convergence or recover a transferable expert reward. Inherits GANs and apprenticeship/IRL methods.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Learner requires its own environment interactions; absence of expert queries or external rewards is not offline demonstration-only training. The finite-state/action duality analysis and ideal discriminator optimum do not certify nonlinear GAN/TRPO training convergence or recover a transferable expert reward. Inherits GANs and apprenticeship/IRL methods.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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