Summary of A Survey Of Constraint Formulations in Safe Reinforcement Learning, by Akifumi Wachi et al.
A Survey of Constraint Formulations in Safe Reinforcement Learning
by Akifumi Wachi, Xun Shen, Yanan Sui
First submitted to arxiv on: 3 Feb 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 paper tackles the crucial issue of safety in reinforcement learning (RL) for real-world applications. The authors focus on a constrained criterion approach, which aims to optimize an agent’s policy while adhering to specific safety constraints. Despite recent progress in enhancing safety in RL, there is still a lack of understanding about the field due to the diversity of constraint representations and limited exploration of their interrelations. To address this knowledge gap, the authors present a comprehensive review of representative constraint formulations and curated selection of algorithms designed for each formulation. The paper also delves into the theoretical underpinnings that reveal the mathematical mutual relations among common problem formulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence agents behave safely in real-life situations. Right now, there are many different ways to make AI safer, but it’s hard to understand how they all work together. The authors of this paper tried to fix this by reviewing the most important methods for making AI safe and explaining how they relate to each other. |
Keywords
* Artificial intelligence * Reinforcement learning
