GenPQR recovers normalized rewards in maximum-entropy IRL by estimating the policy with classification and the soft Q-function with regression, providing modular finite-sample guarantees under general function approximation.
Oracle inequalities in empirical risk minimization and sparse recovery problems: Ecole D’Et \'e de Probabilit \'e s de Saint-Flour XXXVIII-2008 , volume 2033
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Inverse Reinforcement Learning with Just Classification and a Few Regressions
GenPQR recovers normalized rewards in maximum-entropy IRL by estimating the policy with classification and the soft Q-function with regression, providing modular finite-sample guarantees under general function approximation.