Summary of Safe and Reliable Training Of Learning-based Aerospace Controllers, by Udayan Mandal et al.
Safe and Reliable Training of Learning-Based Aerospace Controllers
by Udayan Mandal, Guy Amir, Haoze Wu, Ieva Daukantas, Fletcher Lee Newell, Umberto Ravaioli, Baoluo Meng, Michael Durling, Kerianne Hobbs, Milan Ganai, Tobey Shim, Guy Katz, Clark Barrett
First submitted to arxiv on: 9 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO); Systems and Control (eess.SY)
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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 Deep reinforcement learning (DRL) has led to significant advancements in controlling complex domains. However, the lack of transparency hinders its adoption in safety-critical fields like aerospace engineering, where errors can have catastrophic consequences. This paper presents novel techniques for training and verifying DRL controllers to ensure their safe behavior. A design-for-verification approach using k-induction is showcased, along with neural Lyapunov Barrier certificates and their applications on a case study. Furthermore, the paper explores other reachability-based methods that, while not providing guarantees, could be effective for verifying other DRL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep reinforcement learning (DRL) has made big progress in controlling complex things. But it’s hard to understand how these models work, which makes them hard to use in places where mistakes can have bad consequences, like space travel or medical devices. This paper shows new ways to make sure DRL controllers are safe and won’t cause problems. They use a special method called k-induction and also talk about something called neural Lyapunov Barrier certificates that can help. The paper also looks at some other methods that might be helpful for making sure DRL systems work right. |
Keywords
» Artificial intelligence » Reinforcement learning
