EdgeRL: A Light-Weight C/C plus plus Framework for OnDevice Reinforcement Learning
18TH INTERNATIONAL SOC DESIGN CONFERENCE 2021 (ISOCC 2021)(2021)
摘要
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 realtime.
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关键词
Reinforcement Learning, On-device learning, Edge device
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