Szczegóły publikacji
Opis bibliograficzny
TinyRL: towards reinforcement learning on tiny embedded devices / Tomasz SZYDŁO, Prem Prakash Jayaraman, Yinhao Li, Graham Morgan, Rajiv Ranjan // W: CIKM'22 : proceedings of the 31st ACM international conference on Information and Knowledge Management / eds. Mohammad Al Hasan, Li Xiong. — New York : Association for Computing Machinery, Inc. (ACM), cop. 2022. — ISBN: 978-1-4503-9236-5. — S. 4985–4988. — Bibliogr. s. 4988, Abstr. — Publikacja dostępna online od: 2022-10-17
Autorzy (5)
- AGHSzydło Tomasz
- Jayaraman Prem Prakash
- Li Yinhao
- Morgan Graham
- Ranjan Rajiv
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 143153 |
|---|---|
| Data dodania do BaDAP | 2022-10-19 |
| Tekst źródłowy | URL |
| DOI | 10.1145/3511808.3557206 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Creative Commons | |
| Wydawca | Association for Computing Machinery (ACM) |
| Konferencja | ACM International Conference on Information and Knowledge Management 2022 |
Abstract
We observe significant interest in reinforcement learning methods for real-world sensing-control scenarios driven by the sensor data streams. However, the delay introduced to the data by the communication channels may degrade the system's performance. It is especially crucial in the internet of things (IoT), where devices with constraint resources and low throughput networks are used. We demonstrate TinyRL framework, a different approach to this problem, by transferring RL algorithms knowledge to resource-limited devices. Our initial experiments point towards a successful demonstration of our technique using common microcontrollers used in IoT systems. Such devices have limited resource capability, and their regulation by processing data directly on devices without their transmission to the cloud can play a crucial role in their lifespan and usefulness.
