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.

本研究解决了逆强化学习（IRL）在推断奖励函数时常常无法捕捉任务目标的问题。我们提出了一种新颖的框架，侧重于任务对齐，并通过专家示范作为弱监督来生成候选奖励函数，以此训练策略，验证其完成任务的能力。实验结果表明，该框架在复杂和迁移学习场景中优于传统的模仿学习基线。

重新思考逆强化学习：从数据对齐到任务对齐