Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment

• URL: http://arxiv.org/abs/2410.23680v1
• Date: Thu, 31 Oct 2024 07:08:14 GMT
• Title: Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
• Authors: Weichao Zhou, Wenchao Li,
• Abstract summary: imitation learning (IL) algorithms use inverse reinforcement learning (IRL) to infer a reward function that aligns with a demonstration. We propose a novel framework for IRL-based IL that prioritizes task alignment over conventional data alignment.
• Score: 7.477559660351106
• License:
• 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.

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