Summary of A Cmdp-within-online Framework For Meta-safe Reinforcement Learning, by Vanshaj Khattar et al.
A CMDP-within-online framework for Meta-Safe Reinforcement Learning
by Vanshaj Khattar, Yuhao Ding, Bilgehan Sel, Javad Lavaei, Ming Jin
First submitted to arxiv on: 26 May 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 A meta-reinforcement learning framework is developed to address the constraint violations issue in existing works, allowing for more realistic applications in real-world settings. The paper proposes a novel approach called Meta-SRL, which uses a CMDP-within-online framework to establish provable guarantees. The method obtains task-averaged regret bounds for reward maximization and constraint violations using gradient-based meta-learning. Experimental results demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn new tasks by improving their ability to follow rules and avoid mistakes. The researchers created a new way to do this called Meta-SRL, which can work well in real-life situations. They tested it and showed that it gets better at solving problems as the tasks become more similar or related. |
Keywords
» Artificial intelligence » Meta learning » Reinforcement learning
