mlsanityData to 14 Oct 2026

A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization

arXiv 2506.02458 ↗First posted 3 Jun 2025cs.IT cs.LG math.ITcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Mobile edge computing (MEC) allows appliances to offload workloads to neighboring MEC servers that have the potential for computation-intensive tasks with limited computational capabilities. This paper studied how deep reinforcement learning (DRL) algorithms are used in an MEC system to find feasible decentralized dynamic computation offloading strategies, which leads to the construction of an extensible MEC system that operates effectively with finite feedback. Even though the Deep Deterministic Policy Gradient (DDPG) algorithm, subject to their knowledge of the MEC system, can be used to allocate powers of both computation offloading and local execution, to learn a computation offloading policy for each user independently, we realized that this solution still has some inherent weaknesses. Hence, we introduced a new approach for this problem based on the Twin Delayed DDPG algorithm, which enables us to overcome this proneness and investigate cases where mobile users are portable. Numerical results showed that individual users can autonomously learn adequate policies through the proposed approach. Besides, the performance of the suggested solution exceeded the conventional DDPG-based power control strategy.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 5 of its 18 references that are in the map. First posted on arXiv 3 Jun 2025.

Builds on

5 of 18 references.

References · 5

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2017Mobile Edge Computing: A Survey on Architecture and Computation Offloadingcited by 53
  3. 2018Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning Approachcited by 8
  4. 2017Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computingcited by 7
  5. 2021Channel Estimation in MIMO Systems with One-bit Spatial Sigma-delta ADCscited by 2

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  1. 2026On-Device Deep Reinforcement Learning for Decentralized Task Offloading Performance trade-offs in the training process
  2. 2025Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing
  3. 2025Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
  4. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
  5. 2024dRG-MEC: Decentralized Reinforced Green Offloading for MEC-enabled Cloud Network
  6. 2024To Train or Not to Train: Balancing Efficiency and Training Cost in Deep Reinforcement Learning for Mobile Edge Computing
  7. 2024Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing
  8. 2025Energy Efficient Task Offloading in UAV-Enabled MEC Using a Fully Decentralized Deep Reinforcement Learning Approach