# Multi-objective Deep Reinforcement Learning for Mobile Edge Computing

arXiv: https://arxiv.org/abs/2307.14346 · HTML: https://mlsanity.com/paper/2307.14346/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 5 Jul 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks](https://mlsanity.com/paper/1705.00704/) (2017)
- [Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading](https://mlsanity.com/paper/1708.08810/) (2017)
- [Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks](https://mlsanity.com/paper/2001.09223/) (2020)
- [Task Offloading and Replication for Vehicular Cloud Computing: A Multi-Armed Bandit Approach](https://mlsanity.com/paper/1812.04575/) (2018)
- [Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing](https://mlsanity.com/paper/1906.07860/) (2019)

## Built on by (most cited first, 2 of 2)

- [Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2604.07148/) (2026)
- [ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems](https://mlsanity.com/paper/2608.08804/) (2026)

## Abstract (arXiv)

Mobile edge computing (MEC) is essential for next-generation mobile network applications that prioritize various performance metrics, including delays and energy consumption. However, conventional single-objective scheduling solutions cannot be directly applied to practical systems in which the preferences of these applications (i.e., the weights of different objectives) are often unknown or challenging to specify in advance. In this study, we address this issue by formulating a multi-objective offloading problem for MEC with multiple edges to minimize expected long-term energy consumption and transmission delay while considering unknown preferences as parameters. To address the challenge of unknown preferences, we design a multi-objective (deep) reinforcement learning (MORL)-based resource scheduling scheme with proximal policy optimization (PPO). In addition, we introduce a well-designed 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. Simulation results demonstrate that our proposed MORL scheme enhances the hypervolume of the Pareto front by up to 233.1% compared to benchmarks. Our full framework is available at https://github.com/gracefulning/mec_morl_multipolicy.