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 demonstrations. However, the inferred reward function often fails to capture the underlying task objective. 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 signals to derive a set of candidate reward functions that align with the task rather than only with the data. It 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. The complete code are available at https://github.com/zwc662/PAGAR.