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Found 5 papers, 0 papers with code
Inverse Reinforcement Learning via Matching of Optimality Profiles
Given an optimality profile and a small amount of additional supervision, our algorithm fits a reward function, modeled as a neural network, by essentially minimizing the Wasserstein distance between the corresponding induced distribution and the optimality profile.
Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema
Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema.
Understanding the Power and Limitations of Teaching with Imperfect Knowledge
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.
Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints
We study two teaching approaches: learner-agnostic teaching, where the teacher provides demonstrations from an optimal policy ignoring the learner's preferences, and learner-aware teaching, where the teacher accounts for the learner's preferences.
Teaching Inverse Reinforcement Learners via Features and Demonstrations
In this paper, we study the problem of learning from demonstrations in the setting where this is not the case, i. e., where there is a mismatch between the worldviews of the learner and the expert.
