mlsanityData to 7 Oct 2026

An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions

arXiv 2305.09070 ↗First posted 15 May 2023cs.LGcited by 1

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Abstract (arXiv)

Apprenticeship learning (AL) is a process of inducing effective decision-making policies via observing and imitating experts' demonstrations. Most existing AL approaches, however, are not designed to cope with the evolving reward functions commonly found in human-centric tasks such as healthcare, where offline learning is required. In this paper, we propose an offline Time-aware Hierarchical EM Energy-based Sub-trajectory (THEMES) AL framework to tackle the evolving reward functions in such tasks. The effectiveness of THEMES is evaluated via a challenging task -- sepsis treatment. The experimental results demonstrate that THEMES can significantly outperform competitive state-of-the-art baselines.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 62 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

15 of 62 references.

References · 15

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3 more
  1. 2020Strictly Batch Imitation Learning by Energy-based Distribution Matchingcited by 54
  2. 2017Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Datacited by 47
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Citing papers per year: 2024: 1.

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  1. 2024A Generalized Apprenticeship Learning Framework for Modeling Heterogeneous Student Pedagogical Strategiescited by 2