mlsanityData to 14 Oct 2026

Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing

arXiv 2509.10474 ↗First posted 27 Aug 2025eess.SY cs.SYcited by 2

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

Abstract (arXiv)

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy efficiency. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we formulate a multi-objective offloading problem for MEC with multiple edges to minimize the sum of expected long-term energy consumption and delay while considering unknown preferences. To address the challenge of unknown preferences and the potentially diverse MEC systems, we propose a generalizable multi-objective (deep) reinforcement learning (GMORL)-based tasks offloading framework, which employs the Discrete Soft Actor-Critic (Discrete-SAC) method. Our method uses a single policy model to efficiently schedule tasks based on varying preferences and adapt to heterogeneous MEC systems with different CPU frequencies and server quantities. Under the proposed framework, we introduce a histogram-based state encoding method for constructing features for multiple edges in MEC systems, a sophisticated reward function for accurately computing the utilities of delay and energy consumption, and a novel neural network architecture for improving generalization. Simulation results demonstrate that our proposed GMORL scheme enhances the hypervolume of the Pareto front by up to 121.0% compared to benchmarks. Our code are avavilable at https://github.com/gracefulning/Generalizable-Pareto-Optimal-Offloading-with-Reinforcement-Learning-in-Mobile-Edge-Computing

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 37 references that are in the map. First posted on arXiv 27 Aug 2025.

Builds on

9 of 37 references.

References · 9

  1. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  2. 2014A Survey of Multi-Objective Sequential Decision-Makingcited by 242
  3. 2019A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptationcited by 177
  4. 2019Soft Actor-Critic for Discrete Action Settingscited by 174
  5. 2021Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observabilitycited by 109
  6. 2020Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learningcited by 11
  7. 2017Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloadingcited by 10
  8. 2018Task Offloading and Replication for Vehicular Cloud Computing: A Multi-Armed Bandit Approachcited by 6
  9. 2020Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Accelerationcited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computingcited by 1
  2. 2026Online LLM Selection via Constrained Bandits with Time-Varying Demandcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2023Multi-objective Deep Reinforcement Learning for Mobile Edge Computing
  2. 2025A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization
  3. 2025Cooperative Task Offloading through Asynchronous Deep Reinforcement Learning in Mobile Edge Computing for Future Networks
  4. 2025Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures
  5. 2024Distributed Multi-Objective Dynamic Offloading Scheduling for Air-Ground Cooperative MEC
  6. 2025Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks
  7. 2024Latency-Aware Resource Allocation for Mobile Edge Generation and Computing via Deep Reinforcement Learning
  8. 2024Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories