# DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2406.06986 · HTML: https://mlsanity.com/paper/2406.06986/

## Key facts

- Cited by 17 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 23 of its 59 references that are in the map.
- First posted on arXiv 11 Jun 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 23 of 23)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning](https://mlsanity.com/paper/2208.06193/) (2022)
- [Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2003.08839/) (2020)
- [A Brief Survey of Deep Reinforcement Learning](https://mlsanity.com/paper/1708.05866/) (2017)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [Deep Learning for Large-Scale Traffic-Sign Detection and Recognition](https://mlsanity.com/paper/1904.00649/) (2019)
- [Diffusion-based Reinforcement Learning for Edge-enabled AI-Generated Content Services](https://mlsanity.com/paper/2303.13052/) (2023)
- [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023)
- [Mobile Edge Intelligence and Computing for the Internet of Vehicles](https://mlsanity.com/paper/1906.00400/) (2019)
- [AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing](https://mlsanity.com/paper/2303.01896/) (2023)
- [Joint Task Offloading and Resource Allocation for Multi-Server Mobile-Edge Computing Networks](https://mlsanity.com/paper/1705.00704/) (2017)
- [Deep Reinforcement Learning Based Mode Selection and Resource Allocation for Cellular V2X Communications](https://mlsanity.com/paper/2002.05485/) (2020)
- [Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks](https://mlsanity.com/paper/2010.01370/) (2020)
- [Accuracy-Guaranteed Collaborative DNN Inference in Industrial IoT via Deep Reinforcement Learning](https://mlsanity.com/paper/2301.00130/) (2022)
- [GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds](https://mlsanity.com/paper/2307.00777/) (2023)
- [Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14263/) (2021)
- [Distributed Task Replication for Vehicular Edge Computing: Performance Analysis and Learning-based Algorithm](https://mlsanity.com/paper/2002.08833/) (2020)
- [RFID: Towards Low Latency and Reliable DAG Task Scheduling over Dynamic Vehicular Clouds](https://mlsanity.com/paper/2208.12568/) (2022)
- [ProSecutor: Protecting Mobile AIGC Services on Two-Layer Blockchain via Reputation and Contract Theoretic Approaches](https://mlsanity.com/paper/2404.08899/) (2024)
- [Revenue and Energy Efficiency-Driven Delay Constrained Computing Task Offloading and Resource Allocation in a Vehicular Edge Computing Network: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2010.08119/) (2020)
- [Decomposition Theory Meets Reliability Analysis: Processing of Computation-Intensive Dependent Tasks over Vehicular Clouds with Dynamic Resources](https://mlsanity.com/paper/2210.07337/) (2022)

## Built on by (most cited first, 17 of 17)

- [Diffusion Models for Future Networks and Communications: A Comprehensive Survey](https://mlsanity.com/paper/2508.01586/) (2025)
- [DNN Task Assignment in UAV Networks: A Generative AI Enhanced Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2411.08299/) (2024)
- [Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges](https://mlsanity.com/paper/2411.18199/) (2024)
- [Learning the Optimal Path and DNN Partition for Collaborative Edge Inference](https://mlsanity.com/paper/2410.01857/) (2024)
- [Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.13554/) (2025)
- [Two-Timescale Model Caching and Resource Allocation for Edge-Enabled AI-Generated Content Services](https://mlsanity.com/paper/2411.01458/) (2024)
- [Generative AI for Lyapunov Optimization Theory in UAV-based Low-Altitude Economy Networking](https://mlsanity.com/paper/2501.15928/) (2025)
- [Multi-Agent Conditional Diffusion Model with Mean Field Communication as Wireless Resource Allocation Planner](https://mlsanity.com/paper/2510.22969/) (2025)
- [Hierarchical Task Offloading and Trajectory Optimization in Low-Altitude Intelligent Networks Via Auction and Diffusion-based MARL](https://mlsanity.com/paper/2512.11862/) (2025)
- [Generative AI Enabled Matching for 6G Multiple Access](https://mlsanity.com/paper/2411.04137/) (2024)
- [Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion](https://mlsanity.com/paper/2506.21933/) (2025)
- [Federated Reinforcement Learning for Runtime Optimization of AI Applications in Smart Eyewears](https://mlsanity.com/paper/2508.17262/) (2025)
- [Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2509.12716/) (2025)
- [Joint Optimization of Model Partitioning and Resource Allocation for Anti-Jamming Collaborative Inference Systems](https://mlsanity.com/paper/2603.02579/) (2026)
- [Leveraging Large Vision Model for Multi-UAV Co-perception in Low-Altitude Wireless Networks](https://mlsanity.com/paper/2603.16927/) (2026)
- [Lightweight Quantum Agent for Edge Systems: Joint PQC and NOMA Resource Allocation](https://mlsanity.com/paper/2604.25980/) (2026)
- [From Denoising to Decision Making: A Survey on Diffusion Model-Enabled Deep Reinforcement Learning for Wireless Networks](https://mlsanity.com/paper/2605.25531/) (2026)

## Abstract (arXiv)

The rapid advancement of Artificial Intelligence (AI) has introduced Deep Neural Network (DNN)-based tasks to the ecosystem of vehicular networks. These tasks are often computation-intensive, requiring substantial computation resources, which are beyond the capability of a single vehicle. To address this challenge, Vehicular Edge Computing (VEC) has emerged as a solution, offering computing services for DNN-based tasks through resource pooling via Vehicle-to-Vehicle/Infrastructure (V2V/V2I) communications. In this paper, we formulate the problem of joint DNN partitioning, task offloading, and resource allocation in VEC as a dynamic long-term optimization. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. To this end, we first leverage a Lyapunov optimization technique to decouple the original long-term optimization with stability constraints into a per-slot deterministic problem. Afterwards, we propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models to determine the optimal DNN partitioning and task offloading decisions. Furthermore, we integrate convex optimization techniques into MAD2RL as a subroutine to allocate computation resources, enhancing the learning efficiency. Through simulations under real-world movement traces of vehicles, we demonstrate the superior performance of our proposed algorithm compared to existing benchmark solutions.