<ul><li>Soft real-time applications in edge computing pose challenges for task scheduling while meeting timing constraints.</li><li>Schedulers based on heuristic algorithms struggle to adapt to dynamic edge computing environments.</li><li>Agile Reinforcement Learning (aRL) proposed for task scheduling in edge computing enhances predictability and adaptability of RL-agent.</li><li>Experiments show that aRL achieves a higher hit-ratio and converges faster compared to baseline approaches.</li></ul>

Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing

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