Extend the framework beyond the Gumbel-shock softmax specification

Extend the semiparametric framework for debiased inverse reinforcement learning and dynamic discrete choice models, which currently relies on the Gumbel-shock structure underlying the softmax policy, to generalized Gumbel shock families or fully nonparametric shock distributions, thereby accommodating weaker behavioral assumptions.

Background

The paper develops a semiparametric, debiased machine-learning framework for inverse reinforcement learning and dynamic discrete choice that assumes agents follow a softmax policy induced by Gumbel shocks (or equivalently, maximum-entropy regularization). This structural assumption enables exact identification results and efficient influence-function derivations for policy evaluation and normalized rewards.

While powerful, the Gumbel-shock specification is restrictive. Relaxing it to broader extreme-value families or to fully unspecified shock distributions would expand applicability and reduce reliance on strong behavioral modeling assumptions. Doing so requires re-establishing identification and efficiency theory under weaker stochastic choice models.