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Apprenticeship learning (AL) is a process of inducing effectivedecision-making policies via observing and imitating experts' demonstrations.Most existing AL approaches, however, are not designed to cope with theevolving reward functions commonly found in human-centric tasks such ashealthcare, where offline learning is required. In this paper, we propose anoffline Time-aware Hierarchical EM Energy-based Sub-trajectory (THEMES) ALframework to tackle the evolving reward functions in such tasks. Theeffectiveness of THEMES is evaluated via a challenging task -- sepsistreatment. The experimental results demonstrate that THEMES can significantlyoutperform competitive state-of-the-art baselines.

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