Summary of Rethinking Inverse Reinforcement Learning: From Data Alignment to Task Alignment, by Weichao Zhou et al.
Rethinking Inverse Reinforcement Learning: from Data Alignment to Task Alignment
by Weichao Zhou, Wenchao Li
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This novel framework for inverse reinforcement learning (IRL) based imitation learning prioritizes task alignment over data alignment, leveraging expert demonstrations as weak supervision to derive candidate reward functions that align with the task. A semi-supervised approach combines adversarial training with a set of reward functions to validate policy performance. Theoretical insights demonstrate mitigation of task-reward misalignment, and practical implementation outperforms conventional IL baselines in complex and transfer learning scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine scientists trying to teach robots new skills by showing them how to do things. But often, the robots don’t understand what they’re supposed to be doing, even if they can imitate the actions. This paper proposes a new way to help robots learn from demonstrations while keeping track of their goals. It uses a special kind of training that combines expert guidance with a competition between different ways to achieve the goal. The results show that this method is better than usual methods in tricky situations. |
Keywords
» Artificial intelligence » Alignment » Reinforcement learning » Semi supervised » Transfer learning
