# Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks

arXiv: https://arxiv.org/abs/2010.01722 · HTML: https://mlsanity.com/paper/2010.01722/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [Holistic Network Virtualization and Pervasive Network Intelligence for 6G](https://mlsanity.com/paper/2301.00519/) (2023)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Research on Edge Computing and Cloud Collaborative Resource Scheduling Optimization Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2502.18773/) (2025)
- [Structural Knowledge-Driven Meta-Learning for Task Offloading in Vehicular Networks with Integrated Communications, Sensing and Computing](https://mlsanity.com/paper/2402.15972/) (2024)
- [Intelligent Offloading in Vehicular Edge Computing: A Comprehensive Review of Deep Reinforcement Learning Approaches and Architectures](https://mlsanity.com/paper/2502.06963/) (2025)
- [Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality](https://mlsanity.com/paper/2305.16571/) (2023)
- [Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles](https://mlsanity.com/paper/2308.02603/) (2023)
- [Towards Secure and Efficient Data Scheduling for Vehicular Social Networks](https://mlsanity.com/paper/2407.00141/) (2024)
- [Communication-Aware Consistent Edge Selection for Mobile Users and Autonomous Vehicles](https://mlsanity.com/paper/2408.03435/) (2024)
- [Learning Value of Information towards Joint Communication and Control in 6G V2X](https://mlsanity.com/paper/2505.06978/) (2025)

## Abstract (arXiv)

Mobile edge computing (MEC) is a promising technology to support mission-critical vehicular applications, such as intelligent path planning and safety applications. In this paper, a collaborative edge computing framework is developed to reduce the computing service latency and improve service reliability for vehicular networks. First, a task partition and scheduling algorithm (TPSA) is proposed to decide the workload allocation and schedule the execution order of the tasks offloaded to the edge servers given a computation offloading strategy. Second, an artificial intelligence (AI) based collaborative computing approach is developed to determine the task offloading, computing, and result delivery policy for vehicles. Specifically, the offloading and computing problem is formulated as a Markov decision process. A deep reinforcement learning technique, i.e., deep deterministic policy gradient, is adopted to find the optimal solution in a complex urban transportation network. By our approach, the service cost, which includes computing service latency and service failure penalty, can be minimized via the optimal workload assignment and server selection in collaborative computing. Simulation results show that the proposed AI-based collaborative computing approach can adapt to a highly dynamic environment with outstanding performance.