Projekt per år
Machine and reinforcement learning (RL) are increasingly being applied to plan and control the behavior of autonomous systems interacting with the physical world. Examples include self-driving vehicles, distributed sensor networks, and agile robots. However, when machine learning is to be applied in these new settings, the algorithms had better come with the same type of reliability, robustness, and safety bounds that are hallmarks of control theory, or failures could be catastrophic. Thus, as learning algorithms are increasingly and more aggressively deployed in safety critical settings, it is imperative that control theorists join the conversation. The goal of this tutorial paper is to provide a starting point for control theorists wishing to work on learning related problems, by covering recent advances bridging learning and control theory, and by placing these results within an appropriate historical context of system identification and adaptive control.
|Titel på gästpublikation||2019 IEEE 58th Conference on Decision and Control, CDC 2019|
|Förlag||IEEE - Institute of Electrical and Electronics Engineers Inc.|
|Status||Published - 2020 mar 12|
|Evenemang||58th IEEE Conference on Decision and Control, CDC 2019 - Nice, Frankrike|
Varaktighet: 2019 dec 11 → 2019 dec 13
|Namn||Proceedings of the IEEE Conference on Decision and Control|
|Konferens||58th IEEE Conference on Decision and Control, CDC 2019|
|Period||2019/12/11 → 2019/12/13|
FingeravtryckUtforska forskningsämnen för ”From self-tuning regulators to reinforcement learning and back again”. Tillsammans bildar de ett unikt fingeravtryck.
- 2 Aktiva
2015/10/01 → 2029/12/31