Summary of Safety Through Permissibility: Shield Construction For Fast and Safe Reinforcement Learning, by Alexander Politowicz et al.
Safety through Permissibility: Shield Construction for Fast and Safe Reinforcement Learning
by Alexander Politowicz, Sahisnu Mazumder, Bing Liu
First submitted to arxiv on: 29 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 machine learning-based solution for reinforcement learning (RL) problems is proposed in this paper, with a focus on ensuring safety. The authors leverage the concept of “shielding” to enforce user-defined safety specifications and ensure that RL agents behave safely. They introduce a permissibility-based framework that integrates safety considerations into the RL training process, allowing for efficient learning while maintaining safety. Experimental results demonstrate the effectiveness of this approach in three standard RL applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better AI agents that follow rules and don’t cause harm. The authors want to make sure these agents learn safely and efficiently. They use a technique called “shielding” to ensure the agents behave correctly. By combining this with another idea called permissibility, they show how to make RL training safer and faster at the same time. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning
