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Related Experiment Videos

HyAR-PPO: Hybrid Action Representation Learning for Incentive-Driven Task Offloading in Vehicular Edge Computing.

Wentao Wang1, Mingmeng Li2, Honghai Wu1

  • 1The School of Information Engineering, Henan University of Science and Technology, Luoyang 471023, China.

Sensors (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an incentive framework for vehicular edge computing (VEC) that uses assisting vehicles (AVs) to share computing power, maximizing social welfare and balancing profits for all parties involved.

Keywords:
Nash bargainingdeep reinforcement learninghybrid action spacetask offloadingvehicular edge computing

Related Experiment Videos

Area of Science:

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

Background:

  • Roadside Units (RSUs) in Vehicular Edge Computing (VEC) face computational limitations during peak demand.
  • Assisting Vehicles (AVs) can offload tasks to RSUs, but lack incentives for resource sharing.
  • Existing VEC solutions often neglect system-wide welfare optimization and fair profit distribution.

Purpose of the Study:

  • To propose an incentive-driven, utility-balanced task offloading framework for VEC systems.
  • To maximize overall social welfare by jointly optimizing resource allocation and pricing strategies.
  • To address the challenge of incentivizing selfish AVs to contribute their idle computing resources.

Main Methods:

  • Formulated resource allocation as a Mixed-Integer Nonlinear Programming (MINLP) problem.
  • Introduced hybrid action representation learning and the HyAR-PPO algorithm for joint optimization of discrete and continuous VEC decisions.
  • Employed Nash bargaining games to determine equilibrium prices, ensuring individual rationality and Pareto efficiency.

Main Results:

  • The proposed framework effectively coordinates multi-party interests among user vehicles, RSUs, and AVs.
  • The hybrid action representation learning approach significantly improves social welfare compared to existing methods.
  • The benefits of the proposed method are more substantial in medium-to-large-scale VEC scenarios.

Conclusions:

  • The developed incentive framework successfully balances utility and maximizes social welfare in VEC systems.
  • Hybrid action representation learning offers a novel and effective solution for complex VEC optimization problems.
  • The approach provides a fair profit distribution mechanism, encouraging participation and resource sharing in VEC networks.