5 papers
Fast Rates for Inverse Reinforcement Learning
Andreas Schlaginhaufen, Maryam Kamgarpour
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 actio…
Eliciting Truthful Feedback for Preference-Based Learning via the VCG Mechanism
Leo Landolt, Anna Maddux, Andreas Schlaginhaufen +2
We study resource allocation problems in which a central planner allocates resources among strategic agents with private cost functions in order to minimize a social cost, defined…
Efficient Preference-Based Reinforcement Learning: Randomized Exploration Meets Experimental Design
Andreas Schlaginhaufen, Reda Ouhamma, Maryam Kamgarpour
We study reinforcement learning from human feedback in general Markov decision processes, where agents learn from trajectory-level preference comparisons. A central challenge in th…
Convergence of a model-free entropy-regularized inverse reinforcement learning algorithm
Titouan Renard, Andreas Schlaginhaufen, Tingting Ni +1
Given a dataset of expert demonstrations, inverse reinforcement learning (IRL) aims to recover a reward for which the expert is optimal. This work proposes a model-free algorithm t…
Towards the Transferability of Rewards Recovered via Regularized Inverse Reinforcement Learning
Andreas Schlaginhaufen, Maryam Kamgarpour
Inverse reinforcement learning (IRL) aims to infer a reward from expert demonstrations, motivated by the idea that the reward, rather than the policy, is the most succinct and tran…