A Simple Solution for Offline Imitation from Observations and Examples
  with Possibly Incomplete Trajectories
Kai Yan Alexander G. Schwing Yu-Xiong Wang
Offline imitation from observations aims to solve MDPs where only
task-specific expert states and task-agnostic non-expert state-action pairs are
available. Offline imitation is useful in real-world scenarios where arbitrary
interactions are costly and expert actions are unavailable. The
state-of-the-art "DIstribution Correction Estimation" (DICE) methods minimize
divergence of state occupancy between expert and learner policies and retrieve
a policy with weighted behavior cloning; however, their results are unstable
when learning from incomplete trajectories, due to a non-robust optimization in
the dual domain. To address the issue, in this paper, we propose
Trajectory-Aware Imitation Learning from Observations (TAILO). TAILO uses a
discounted sum along the future trajectory as the weight for weighted behavior
cloning. The terms for the sum are scaled by the output of a discriminator,
which aims to identify expert states. Despite simplicity, TAILO works well if
there exist trajectories or segments of expert behavior in the task-agnostic
data, a common assumption in prior work. In experiments across multiple
testbeds, we find TAILO to be more robust and effective, particularly with
incomplete trajectories.