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Prune-Based Deep Reinforcement Learning Offloading Algorithm for Mobile Edge Computing
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DOI：10.1109/TCCN.2025.3620385.png)
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Abstract
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Task offloading strategies in Mobile Edge Computing (MEC) aim to reduce computation delay and energy consumption of mobile devices by offloading tasks to edge servers, which is key to improving MEC system performance and user experience. Recent efforts have focused on utilizing deep reinforcement learning (DRL) but DRL-based offloading strategies struggle to achieve optimal decisions in limited iterations due to complexity. Therefore, to address the complexity challenge, this paper proposes a Prune-based Deep reinforcement learning Offloading Algorithm (PDOA) to enhance MEC system performance. First, we construct a dynamic MEC system model and formulate the task offloading problem as a Markov decision process to minimize the total cost of the MEC system. Next, we propose a prune-based DRL offloading algorithm, which prunes DRL models to reduce the complexity and improve learning efficiency, thereby lowering system costs. The experimental results show that PDOA reduces the computational cost of MEC systems significantly compared with other methods and lowers system costs by over 10%. This optimization approach provides a novel research perspective for applying DRL models in MEC.
Keywords:
Mobile edge computing
task offloading
Markov decision process
deep reinforcement learning
prune
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Deep Reinforcement Learning for Energy-Efficient Computation Offloading in Mobile-Edge Computing
PREAI
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