Lecture note proving equivalence between entropy-regularized IRL and DDC models, surveying classical identification methods like Rust's NFXP and Hotz-Miller CCP, modern ML approaches like IQ-Learn, and closing with an ERM gradient estimator.
Learning near-optimal policies with Bellman- residual minimization based fitted policy iteration and a single sample path
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A Lecture Note on Offline RL and IRL, Part II: Foundations of Inverse Reinforcement Learning and Dynamic Discrete Choice Models
Lecture note proving equivalence between entropy-regularized IRL and DDC models, surveying classical identification methods like Rust's NFXP and Hotz-Miller CCP, modern ML approaches like IQ-Learn, and closing with an ERM gradient estimator.