  Fashion is a complex social phenomenon. People follow fashion styles from
demonstrations by experts or fashion icons. However, for machine agent,
learning to imitate fashion experts from demonstrations can be challenging,
especially for complex styles in environments with high-dimensional, multimodal
observations. Most existing research regarding fashion outfit composition
utilizes supervised learning methods to mimic the behaviors of style icons.
These methods suffer from distribution shift: because the agent greedily
imitates some given outfit demonstrations, it can drift away from one style to
another styles given subtle differences. In this work, we propose an
adversarial inverse reinforcement learning formulation to recover reward
functions based on hierarchical multimodal representation (HM-AIRL) during the
imitation process. The hierarchical joint representation can more
comprehensively model the expert composited outfit demonstrations to recover
the reward function. We demonstrate that the proposed HM-AIRL model is able to
recover reward functions that are robust to changes in multimodal observations,
enabling us to learn policies under significant variation between different
styles.
