EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sang-Soo | - |
dc.contributor.author | Kim, Dong-Hee | - |
dc.contributor.author | Kang, Jun-Gu | - |
dc.contributor.author | Chung, Ki Seok | - |
dc.date.accessioned | 2022-07-06T11:33:12Z | - |
dc.date.available | 2022-07-06T11:33:12Z | - |
dc.date.created | 2022-03-07 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.issn | 2163-9612 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140371 | - |
dc.description.abstract | Advances in reinforcement learning (RL) have achieved significant success in many areas. However, RL typically requires a large amount of computation and memory. Often RL implemented in Python is too heavy to run on a resource-limited edge device. Therefore, making the RL model lighter is very important for on-device machine learning. In this paper, we propose a lightweight C/C++ RL framework aiming for RL on edge devices. The proposed RL framework is designed to run on a single-core processor that is typically included in a resource-limited embedded platform. The evaluation using OpenAI Gym's CartPole demonstration shows that the model can be trained on an edge device in real-Time. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chung, Ki Seok | - |
dc.identifier.doi | 10.1109/ISOCC53507.2021.9613916 | - |
dc.identifier.scopusid | 2-s2.0-85123352028 | - |
dc.identifier.wosid | 000861550500102 | - |
dc.identifier.bibliographicCitation | Proceedings - International SoC Design Conference 2021, ISOCC 2021, pp.235 - 236 | - |
dc.relation.isPartOf | Proceedings - International SoC Design Conference 2021, ISOCC 2021 | - |
dc.citation.title | Proceedings - International SoC Design Conference 2021, ISOCC 2021 | - |
dc.citation.startPage | 235 | - |
dc.citation.endPage | 236 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Hardware & Architecture | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordAuthor | Edge device | - |
dc.subject.keywordAuthor | On-device learning | - |
dc.subject.keywordAuthor | Reinforcement Learning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9613916 | - |
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