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Related Experiment Video

Updated: Jul 16, 2025

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
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Task Offloading Decision-Making Algorithm for Vehicular Edge Computing: A Deep-Reinforcement-Learning-Based Approach.

Wei Shi1,2, Long Chen1,2, Xia Zhu1,2

  • 1School of Computer Science and Engineering, Southeast University, Nanjing 211189, China.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

Efficient task offloading in vehicular edge computing (VEC) is optimized using a novel deep deterministic policy gradient algorithm. This TODM_DDPG approach minimizes system costs by jointly optimizing task scheduling and offloading proportions, outperforming existing methods.

Keywords:
computation offloadingdeep reinforcement learningvehicular edge computing

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Area of Science:

  • Vehicular Edge Computing (VEC)
  • Distributed Systems
  • Artificial Intelligence

Background:

  • Efficient task offloading is critical for vehicular edge computing (VEC) to meet performance demands while minimizing resource consumption.
  • Conventional methods often neglect vehicle mobility and fail to fully optimize server resource utilization.
  • Existing distributed task offloading decisions rely on local vehicle states, limiting overall system efficiency.

Purpose of the Study:

  • To propose a novel task offloading decision-making algorithm for a cloud-edge-vehicle three-tier VEC system.
  • To jointly optimize task scheduling and offloading proportions under vehicle mobility constraints.
  • To minimize the total system cost, considering delay, energy consumption, and resource competition.

Main Methods:

  • Development of a task offloading decision-making algorithm based on deep deterministic policy gradient (TODM_DDPG).
  • Utilizing an actor-critic framework where the actor network outputs deterministic policies and the critic network evaluates actions.
  • Conducting parameter tuning experiments and comparative analyses with baseline algorithms like Deep Q Network (DQN) and Actor-Critic (AC).

Main Results:

  • The proposed TODM_DDPG algorithm effectively handles the non-convexity and high-dimensional state space of the optimization problem.
  • Parameter tuning experiments identified optimal hyper-parameters for the algorithm.
  • Comparative experiments demonstrated that TODM_DDPG outperforms baseline algorithms in reducing system costs.

Conclusions:

  • The TODM_DDPG algorithm offers a superior approach to task offloading decision-making in VEC systems.
  • The proposed method achieves an average performance improvement of approximately 13% in reducing system costs compared to baseline algorithms.
  • This research contributes to more efficient resource management and task execution in dynamic vehicular environments.