  Inverse Reinforcement Learning (IRL) aims to reconstruct the reward function
from expert demonstrations to facilitate policy learning, and has demonstrated
its remarkable success in imitation learning. To promote expert-like behavior,
existing IRL methods mainly focus on learning global reward functions to
minimize the trajectory difference between the imitator and the expert.
However, these global designs are still limited by the redundant noise and
error propagation problems, leading to the unsuitable reward assignment and
thus downgrading the agent capability in complex multi-stage tasks. In this
paper, we propose a novel Curricular Subgoal-based Inverse Reinforcement
Learning (CSIRL) framework, that explicitly disentangles one task with several
local subgoals to guide agent imitation. Specifically, CSIRL firstly introduces
decision uncertainty of the trained agent over expert trajectories to
dynamically select subgoals, which directly determines the exploration boundary
of different task stages. To further acquire local reward functions for each
stage, we customize a meta-imitation objective based on these curricular
subgoals to train an intrinsic reward generator. Experiments on the D4RL and
autonomous driving benchmarks demonstrate that the proposed methods yields
results superior to the state-of-the-art counterparts, as well as better
interpretability. Our code is available at https://github.com/Plankson/CSIRL.
