Summary of Cat: Caution Aware Transfer in Reinforcement Learning Via Distributional Risk, by Mohamad Fares El Hajj Chehade et al.
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk
by Mohamad Fares El Hajj Chehade, Amrit Singh Bedi, Amy Zhang, Hao Zhu
First submitted to arxiv on: 16 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Transfer learning in reinforcement learning has become crucial for improving data efficiency in new tasks by leveraging knowledge from previously learned tasks. However, current state-of-the-art methods often fall short in ensuring safety during the transfer process, particularly when unforeseen risks emerge. This work addresses these limitations by introducing a novel Caution-Aware Transfer Learning (CAT) framework that optimizes a weighted sum of reward return and caution-based on state-action occupancy measures. Our contributions include proposing the CAT framework, deriving theoretical sub-optimality bounds, and empirically validating its efficacy in delivering safer policies under varying risk conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning to help robots learn new tasks quickly and safely. It’s like when you’re trying to learn a new skill, but instead of just relying on luck, you can use what you already know to help you get better faster. The problem is that current methods don’t always do this safely, so they might get stuck or make mistakes. This paper introduces a new way to transfer knowledge from one task to another while making sure it’s done safely and effectively. They show that their method works well in different situations and can even help robots avoid taking risks when needed. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Transfer learning
