Fast Rates for Inverse Reinforcement Learning
Quick summary
arXiv:2605.14599v2 Announce Type: replace-cross Abstract: 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 Hes
Key takeaways
- arXiv:2605.14599v2 Announce Type: replace-cross Abstract: 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 Hes
Why it matters
“Fast Rates for Inverse Reinforcement Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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