Extend analysis to nonhomogeneous MDPs, finite-horizon settings, and dependent transitions

Extend the semiparametric inference framework for debiased inverse reinforcement learning to cover nonhomogeneous Markov decision processes, finite-horizon decision problems, and models with dependence across transitions, determining conditions under which identification, efficient influence functions, and efficient estimators remain valid.

Background

The paper’s theory and estimators are developed for time-homogeneous, infinite-horizon MDPs with i.i.d. one-step samples and stationary dynamics, settings that simplify identification and efficiency derivations. Many real-world sequential decision processes are nonstationary, have fixed finite horizons, or exhibit temporal dependence (e.g., longitudinal panels or Markov chains beyond i.i.d. transitions).

Extending the analysis to these more general environments would broaden practical relevance and require new technical work to handle nonstationarity, horizon effects, and dependence in sampling and nuisance estimation while preserving valid asymptotic inference.