The QECO algorithm captures the dynamics of the MEC environment by integrating the Dueling Double Deep Q-Network (D3QN) model with Long Short-Term Memory (LSTM) networks to maximize QoE.
By integrating both double Q-learning and dueling network architectures, D3QN overcomes overestimation bias in action-value predictions, improving the model's ability for accurate predictions and better offloading strategies.
Incorporating LSTM networks allows for continuous estimation of dynamic workloads at edge servers, enabling mobile devices to adapt their offloading strategies effectively in a fluctuating MEC environment.
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