Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment

TL;DR

This paper introduces a semi-supervised IRL framework that emphasizes task alignment over data alignment, improving imitation learning performance in complex and transfer scenarios.

Contribution

It proposes a novel IRL-based imitation learning framework that prioritizes task objectives using weak supervision and adversarial training, addressing reward misalignment issues.

Findings

Outperforms traditional IL methods in complex tasks

Effective in transfer learning scenarios

Theoretically mitigates reward misalignment

Abstract

Many imitation learning (IL) algorithms use inverse reinforcement learning (IRL) to infer a reward function that aligns with the demonstration. However, the inferred reward functions often fail to capture the underlying task objectives. In this paper, we propose a novel framework for IRL-based IL that prioritizes task alignment over conventional data alignment. Our framework is a semi-supervised approach that leverages expert demonstrations as weak supervision to derive a set of candidate reward functions that align with the task rather than only with the data. It then adopts an adversarial mechanism to train a policy with this set of reward functions to gain a collective validation of the policy's ability to accomplish the task. We provide theoretical insights into this framework's ability to mitigate task-reward misalignment and present a practical implementation. Our experimental results show that our framework outperforms conventional IL baselines in complex and transfer learning scenarios.