  The Inverse Reinforcement Learning (\textit{IRL}) problem has seen rapid
evolution in the past few years, with important applications in domains like
robotics, cognition, and health. In this work, we explore the inefficacy of
current IRL methods in learning an agent's reward function from expert
trajectories depicting long-horizon, complex sequential tasks. We hypothesize
that imbuing IRL models with structural motifs capturing underlying tasks can
enable and enhance their performance. Subsequently, we propose a novel IRL
method, SMIRL, that first learns the (approximate) structure of a task as a
finite-state-automaton (FSA), then uses the structural motif to solve the IRL
problem. We test our model on both discrete grid world and high-dimensional
continuous domain environments. We empirically show that our proposed approach
successfully learns all four complex tasks, where two foundational IRL
baselines fail. Our model also outperforms the baselines in sample efficiency
on a simpler toy task. We further show promising test results in a modified
continuous domain on tasks with compositional reward functions.
