Fast Rates for Inverse Reinforcement Learning
We establish novel structural and statistical results for entropy-regularized min-max inverse reinforcement learning (Min-Max-IRL) in finite-horizon MDPs with Borel state and action spaces. We show that maximum likelihood estimation (MLE) and Min-Max-IRL are equivalent at the population level, and at the empirical level under deterministic dynamics. For linear reward classes, we leverage pseudo-self-concordance of the Min-Max-IRL loss to prove that both the excess trajectory-level KL divergence and the squared parameter error in the Hessian norm decay at the fast rate $O(n^{-1})$, where $n$ is the number of expert trajectories. A local minimax lower bound matches the parameter-error rate ...
| Authors | Andreas Schlaginhaufen, Maryam Kamgarpour |
|---|---|
| Primary category | cs.LG |
| Published | 14 May 2026 |
| Last updated at source | 6 August 2026 |
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