4 citations · 6 across the 6 of their papers we have counts for
7 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…
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…
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…
Identifiability and Generalizability in Constrained Inverse Reinforcement Learning
Andreas Schlaginhaufen, Maryam Kamgarpour
Two main challenges in Reinforcement Learning (RL) are designing appropriate reward functions and ensuring the safety of the learned policy. To address these challenges, we present…
Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems
Andreas Schlaginhaufen, Philippe Wenk, Andreas Krause +1
Learning how complex dynamical systems evolve over time is a key challenge in system identification. For safety critical systems, it is often crucial that the learned model is guar…