EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning
- Authors
- Park, Sang-Soo; Kim, Dong-Hee; Kang, Jun-Gu; Chung, Ki Seok
- Issue Date
- Nov-2021
- Publisher
- IEEE
- Keywords
- Edge device; On-device learning; Reinforcement Learning
- Citation
- Proceedings - International SoC Design Conference 2021, ISOCC 2021, pp.235 - 236
- Indexed
- SCOPUS
- Journal Title
- Proceedings - International SoC Design Conference 2021, ISOCC 2021
- Start Page
- 235
- End Page
- 236
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140371
- DOI
- 10.1109/ISOCC53507.2021.9613916
- ISSN
- 2163-9612
- 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.
- Files in This Item
-
Go to Link
- Appears in
Collections - 서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140371)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.