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GAIL: adversarial state-action occupancy matching

Connected entropy-regularized inverse learning to occupancy-measure matching and trained policies against a discriminator of expert versus learner state-action samples.

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Connected entropy-regularized inverse learning to occupancy-measure matching and trained policies against a discriminator of expert versus learner state-action samples.

Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.

Scientific scope

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.

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People and systems

Jonathan Ho; Stefano Ermon

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.

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English catalogue narrative

GAIL: adversarial state-action occupancy matching

Connected entropy-regularized inverse learning to occupancy-measure matching and trained policies against a discriminator of expert versus learner state-action samples.

Scientific scope

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.

Reading status

This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.

People and systems

Jonathan Ho; Stefano Ermon

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.

Original sources