Safe and Reliable Training of Learning-Based Aerospace Controllers
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913423425536000 |
|---|---|
| author | Mandal, Udayan Amir, Guy Wu, Haoze Daukantas, Ieva Newell, Fletcher Lee Ravaioli, Umberto Meng, Baoluo Durling, Michael Hobbs, Kerianne Ganai, Milan Shim, Tobey Katz, Guy Barrett, Clark |
| author_facet | Mandal, Udayan Amir, Guy Wu, Haoze Daukantas, Ieva Newell, Fletcher Lee Ravaioli, Umberto Meng, Baoluo Durling, Michael Hobbs, Kerianne Ganai, Milan Shim, Tobey Katz, Guy Barrett, Clark |
| contents | In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and safety-critical domains, in which a single mistake can have dire consequences. In this paper, we present novel advancements in both the training and verification of DRL controllers, which can help ensure their safe behavior. We showcase a design-for-verification approach utilizing k-induction and demonstrate its use in verifying liveness properties. In addition, we also give a brief overview of neural Lyapunov Barrier certificates and summarize their capabilities on a case study. Finally, we describe several other novel reachability-based approaches which, despite failing to provide guarantees of interest, could be effective for verification of other DRL systems, and could be of further interest to the community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_07088 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Safe and Reliable Training of Learning-Based Aerospace Controllers Mandal, Udayan Amir, Guy Wu, Haoze Daukantas, Ieva Newell, Fletcher Lee Ravaioli, Umberto Meng, Baoluo Durling, Michael Hobbs, Kerianne Ganai, Milan Shim, Tobey Katz, Guy Barrett, Clark Artificial Intelligence Logic in Computer Science Systems and Control In recent years, deep reinforcement learning (DRL) approaches have generated highly successful controllers for a myriad of complex domains. However, the opaque nature of these models limits their applicability in aerospace systems and safety-critical domains, in which a single mistake can have dire consequences. In this paper, we present novel advancements in both the training and verification of DRL controllers, which can help ensure their safe behavior. We showcase a design-for-verification approach utilizing k-induction and demonstrate its use in verifying liveness properties. In addition, we also give a brief overview of neural Lyapunov Barrier certificates and summarize their capabilities on a case study. Finally, we describe several other novel reachability-based approaches which, despite failing to provide guarantees of interest, could be effective for verification of other DRL systems, and could be of further interest to the community. |
| title | Safe and Reliable Training of Learning-Based Aerospace Controllers |
| topic | Artificial Intelligence Logic in Computer Science Systems and Control |
| url | https://arxiv.org/abs/2407.07088 |