Jul 2026· International Journal of Innovative Science and Research Technology· 0 citations· 62 references
TL;DR
A reinforcement learning-based computation offloading strategy using an improved Deep Deterministic Policy Gradient (IDDPG) algorithm that outperforms greedy offloading strategies and demonstrates improved efficiency in dynamic MEC environments.
Abstract
The rapid growth of real-time and computation-intensive applications, such as face recognition, virtual reality, 3D
gaming, augmented reality, and intelligent transportation systems, has significantly increased the demand for efficient data
processing and low-latency services. However, mobile devices are constrained by limited computational capabilities and
battery capacity, making them unsuitable for executing heavy workloads locally. Mobile Edge Computing (MEC) has
emerged as a promising paradigm to offload computation tasks to nearby edge servers, thereby reducing latency and
improving service quality. Despite its advantages, task offloading in MEC environments remains challenging due to the
distributed nature of edge resources, energy constraints of end devices, and dynamic net- work conditions. Existing solutions
based on heuristic methods, genetic algorithms, NOMA-based techniques, and mobility-aware services often suffer from
high latency, excessive energy consumption, and task migration overhead. To address these limitations, this paper
investigates a reinforcement learning-based computation offloading strategy using an improved Deep Deterministic Policy
Gradient (IDDPG) algorithm. The proposed IDDPG approach enables decentralized decision-making by learning optimal
offloading policies from local observations, effectively balancing local execution and task offloading. By minimizing
computation costs, power consumption, and latency, the proposed method outperforms greedy offloading strategies and
demonstrates improved efficiency in dynamic MEC environments.
With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems.
Jingjing Qu, Peiying Zhang, Ruixin Wang et al.· Information· 0 citations
An adaptive Beta-policy and delayed-update multi-agent soft actor-critic method, abbreviated as ABDMASAC, which uses a Beta policy to model bounded actions and achieves a better overall trade-off than the selected MASAC-backbone and on-policy MARL baselines under the considered simulation settings.
Zheng Yao, Jie Liu, Changjun Deng et al.· Computers, Materials & C...· 0 citations
Simulation results confirm that the proposed JORC framework substantially reduces latency, energy consumption, and overall system cost, while increasing the successful task completion ratio compared to existing baseline approaches.
Tanmay Baidya, S. Moh· Italian National Conference...· 0 citations
The growing smart devices (SDs) in the Industrial Internet of Things (IIoT) generate complex computations that strain the performance and energy of local processing. Mobile Edge Computing (MEC) addresses this by providing nearby computing resources for low-latency offloading. However, achieving efficient computation offloading under massive device concurrency and densely distributed computation offloadings remains a key challenge. To address this, this paper constructs a multi-server MEC system model for IIoT and introduces Mean-Field Game (MFG) theory to model the offloading competition among SDs. This effectively reduces the dimensionality and complexity of multi-agent interactions. A novel Mean-Field Computation Offloading (MFCO) algorithm is proposed, which combines MFG with Rainbow Deep Q-Network under a Multi-Agent Deep Reinforcement Learning framework. By incorporating advanced components such as distributional value estimation, prioritized experience replay, multi-step learning, and dueling architecture, each SD acts as an autonomous agent, optimizing its policy based on local observations and mean-field approximations. Further enhancements include Boltzmann exploration, adaptive learning rates, and a mean Q-network structure, which improve convergence speed and training stability. Extensive simulations on a large-scale IIoT platform (100 SDs, 9 MEC servers) demonstrate that MFCO reduces computation latency and improves long-term rewards while maintaining robust server performance.
Xinmin Cheng, Chengquan Yu, Lu Gao et al.· IEEE Transactions on Green C...· 0 citations
A constraint-aware multi-agent edge collaborative offloading algorithm (CARE-CTDE) that achieves better scheduling performance, resource utilization, and constraint satisfaction than baseline methods in dynamic heterogeneous MEC scenarios, demonstrating its effectiveness and robustness for constrained edge computing systems.
Yuxuan Yang, Hexing Wang, Yang Zhou· Mathematics· 0 citations
Comparative tests with PPO, FIFO, FAIR and HAS baselines confirm that multi-agent reinforcement learning can well capture the intrinsic scheduling patterns of complex mobile environments, providing an adaptive and energy-efficient scheduling solution for practical IoT deployments.
Haoyu Gu· Scientific Journal of Intell...· 0 citations