12 citations · 13 across the 2 of their papers we have counts for
4 papers · 1 filter
Inverse Reinforcement Learning via Matching of Optimality Profiles
Luis Haug, Ivan Ovinnikov, Eugene Bykovets
The goal of inverse reinforcement learning (IRL) is to infer a reward function that explains the behavior of an agent performing a task. The assumption that most approaches make is…
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
Rati Devidze, Farnam Mansouri, Luis Haug +2
Machine teaching studies the interaction between a teacher and a student/learner where the teacher selects training examples for the learner to learn a specific task. The typical a…
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
Sebastian Tschiatschek, Ahana Ghosh, Luis Haug +2
Inverse reinforcement learning (IRL) enables an agent to learn complex behavior by observing demonstrations from a (near-)optimal policy. The typical assumption is that the learner…
Teaching Inverse Reinforcement Learners via Features and Demonstrations
Luis Haug, Sebastian Tschiatschek, Adish Singla
Learning near-optimal behaviour from an expert's demonstrations typically relies on the assumption that the learner knows the features that the true reward function depends on. In…