AIRL: reward and shaping separation in adversarial inverse learning

What changed

Used a structured adversarial discriminator separating a reward approximation from potential-based shaping, enabling reward reuse under tested changes in environment dynamics.

Scientific scope

Used a structured adversarial discriminator separating a reward approximation from potential-based shaping, enabling reward reuse under tested changes in environment dynamics. IRL reward ambiguity remains. Exact source recovery discussion restricts to deterministic environments, state-only ground-truth reward, suitable representation and optimal training; no general unique reward identification in stochastic MDPs. Inherits MaxEnt IRL/GAN-guided cost learning and requires learner environment interactions; no independent theorem audit.

Evidence

The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. IRL reward ambiguity remains. Exact source recovery discussion restricts to deterministic environments, state-only ground-truth reward, suitable representation and optimal training; no general unique reward identification in stochastic MDPs. Inherits MaxEnt IRL/GAN-guided cost learning and requires learner environment interactions; no independent theorem audit.

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.

• Learning Robust Rewards with Adversarial Inverse Reinforcement Learning

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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