@inproceedings{
zhou2024rethinking,
title={Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment},
author={Weichao Zhou and Wenchao Li},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=VFRyS7Wx08}
}
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.
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
Weichao Zhou Wenchao Li