Title: Burn-In Demonstrations for Multi-Modal Imitation Learning,
Abstract: Recent work on imitation learning has generated policies that reproduce
expert behavior from multi-modal data. However, past approaches have focused
only on recreating a small number of distinct, expert maneuvers, or have relied
on supervised learning techniques that produce unstable policies. This work
extends InfoGAIL, an algorithm for multi-modal imitation learning, to reproduce
behavior over an extended period of time. Our approach involves reformulating
the typical imitation learning setting to include "burn-in demonstrations" upon
which policies are conditioned at test time. We demonstrate that our approach
outperforms standard InfoGAIL in maximizing the mutual information between
predicted and unseen style labels in road scene simulations, and we show that
our method leads to policies that imitate expert autonomous driving systems
over long time horizons.