An Offline Time-aware Apprenticeship Learning Framework for Evolving
  Reward Functions
Xi Yang Ge Gao Min Chi
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.