Towards Generalisable Imitation Learning Through Conditioned Transition Estimation and Online Behaviour Alignment

TL;DR

This paper introduces UfO, an unsupervised imitation learning method that estimates true actions from observations and aligns agent behavior with teachers, outperforming existing ILfO methods in generalization and stability.

Contribution

The paper proposes a novel two-stage unsupervised imitation learning framework that estimates true actions and aligns behaviors, addressing key limitations of prior ILfO approaches.

Findings

UfO outperforms existing ILfO methods in five environments.

UfO demonstrates the smallest standard deviation, indicating better generalization.

UfO effectively estimates true actions without supervision.

Abstract

State-of-the-art imitation learning from observation methods (ILfO) have recently made significant progress, but they still have some limitations: they need action-based supervised optimisation, assume that states have a single optimal action, and tend to apply teacher actions without full consideration of the actual environment state. While the truth may be out there in observed trajectories, existing methods struggle to extract it without supervision. In this work, we propose Unsupervised Imitation Learning from Observation (UfO) that addresses all of these limitations. UfO learns a policy through a two-stage process, in which the agent first obtains an approximation of the teacher's true actions in the observed state transitions, and then refines the learned policy further by adjusting agent trajectories to closely align them with the teacher's. Experiments we conducted in five widely used environments show that UfO not only outperforms the teacher and all other ILfO methods but also displays the smallest standard deviation. This reduction in standard deviation indicates better generalisation in unseen scenarios.