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

Xi Yang; Ge Gao; Min Chi

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arXiv · 2305.09070

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

Xi Yang·Ge Gao·Min Chi

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Abstract

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.

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Xi Yang, Ge Gao, Min Chi. 2023-05-15. An Offline Time-aware Apprenticeship Learning Framework for Evolving Reward Functions. https://arxiv.org/abs/2305.09070

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