1. Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
- Author
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Zhou, Weichao and Li, Wenchao
- Subjects
Computer Science - Machine Learning ,Computer Science - Artificial Intelligence - 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., Comment: arXiv admin note: substantial text overlap with arXiv:2306.01731
- Published
- 2024