aRL, a DQN-based edge scheduler with EDF-guided exploration and action masking, achieves higher hit-ratio and faster convergence than vanilla RL and heuristic baselines in simulated soft real-time task scheduling.
A comprehensive survey on reinforcement- learning-based computation offloading techniques in edge computing systems
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Agile Reinforcement Learning for Real-Time Task Scheduling in Edge Computing
aRL, a DQN-based edge scheduler with EDF-guided exploration and action masking, achieves higher hit-ratio and faster convergence than vanilla RL and heuristic baselines in simulated soft real-time task scheduling.