Deep Reinforcement Learning for Delay and Energy-Aware Task Scheduling in Edge Clouds
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Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-9637-7_32
Reference13 articles.
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3. Wang, X., Ma, L., Li, H., et al.: Digital twin-assisted efficient reinforcement learning for edge task scheduling. In: 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), pp. 1–5 (2022)
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1. Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices;Sensors;2024-07-27
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