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 ...
In this paper, we propose a lightweight C/C++ RL framework called EdgeRL aiming for RL on resource-limited edge devices. EdgeRL is designed to run on a single- ...
In this paper, we propose a lightweight C/C++ RL framework called EdgeRL aiming for RL on resource- limited edge devices. EdgeRL is designed to run on a single-.
EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning. October 2021. DOI:10.1109/ISOCC53507.2021.9613916. Conference: 2021 18th ...
We explore the state-of-the-art techniques and approaches used to train and optimize ML models in edge devices, highlighting their advantages and making a broad ...
Expertise. deep learning. Present. self-supervised learning. Present. Suggest ... EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning.
Oct 11, 2024 · These methods aim to optimize ML models to fit within the constraints of edge devices, thereby rendering them suitable for training in resource- ...
EdgeRL: A Light-Weight C/C++ Framework for On-Device Reinforcement Learning ... CNA-TTA: Clean and Noisy Region Aware Feature Learning within Clusters for Online- ...
International Conference on Computer Vision Theory and Applications, 2022. ○␣ EdgeRL: A Light-Weight C/C++ Framework for On Device Reinforcement Learning.
Oct 16, 2024 · In this paper, we propose EdgeRL framework that seeks to strike such balance by using an Advantage Actor-Critic (A2C) Reinforcement Learning (RL) ...
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