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Conference Paper
Efficient Deep Reinforcement Learning Framework in Edge Computing Environments
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- Authors
- Wan-Seon Lim, Yeon-Hee Lee
- Issue Date
- 2024-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2024, pp.689-694
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- In this paper, we propose a new framework for deep reinforcement learning in edge computing environments. First, we describe the application of distributed reinforcement learning techniques from previous research to edge computing environments. Based on network delay and the computational capabilities, we propose a framework that enhances the learning efficiency of deep reinforcement learning algorithms. In the proposed framework, the roles of the actors and the learner, running on the edge and cloud servers respectively, are dynamically adjusted based on available resources and network latency. The performance of the proposed framework is validated through experiments on a testbed.
- KSP Keywords
- Cloud server, Deep reinforcement learning, Edge Computing, Learning framework, Network Delay, Network latency, Reinforcement learning(RL), computational capabilities, learning algorithm, learning efficiency