Summary of Safe and Robust Reinforcement Learning: Principles and Practice, by Taku Yamagata et al.
Safe and Robust Reinforcement Learning: Principles and Practice
by Taku Yamagata, Raul Santos-Rodriguez
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Systems and Control (eess.SY)
GrooveSquid.com Paper Summaries
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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 explores the challenges of deploying Reinforcement Learning (RL) systems in real-world scenarios, focusing on safety and robustness concerns. The authors identify key dimensions of the safe and robust RL landscape, including algorithmic, ethical, and practical considerations. They conduct a comprehensive review of recent methodologies and open problems to address the inherent risks associated with RL applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure artificial intelligence (AI) systems are safe and reliable when we use them in real life. It looks at three main areas: how AI works, what’s right or wrong about using it, and how to make it work well. The authors review recent attempts to solve the problems that come with using AI in different situations. |
Keywords
* Artificial intelligence * Reinforcement learning
