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This content will become publicly available on December 10, 2025

Title: Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
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.  more » « less
Award ID(s):
2340776
PAR ID:
10582383
Author(s) / Creator(s):
;
Publisher / Repository:
2024 Conference on Neural Information Processing Systems
Date Published:
Subject(s) / Keyword(s):
Task alignment Inverse reinforcement learning Imitation learning Reinforcement learning
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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