Literature Database Entry

memedi2026towards

Agon Memedi, Negin Masoudifar, Torsten Braun and Falko Dressler, "Towards Generalizable RL-Based Task Offloading for Vehicular Edge Computing," Proceedings of 17th IEEE Vehicular Networking Conference (VNC 2026), Montréal, Canada, June 2026. (to appear)

Abstract

Deep reinforcement learning (DRL) has been used very successfully for a variety of applications in the field of vehicular networking. We investigate the generalizability of a DRL-based scheduling approach for task offloading in vehicular edge computing (VEC). We propose a hybrid scheduling policy that combines heuristic task prioritization with a learned DQN-based resource selection. Two deep Q-learning (DQN) architectures are compared: a conventional multilayer perceptron (MLP) with fixed-size inputs; and a permutation-invariant architecture that uses a lightweight transformer encoder. Trained exclusively on a simple straight-road scenario, the permutation-invariant DQN achieves perfect selection of the fastest processing vehicle when tested in an unseen realistic scenario. In contrast, the MLP-based DQN suffers severe degradation due to positional bias and padding artifacts. Results demonstrate that set-based, permutation-invariant representations are essential for sample efficiency and generalization in VEC environments with high variability.

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Agon Memedi
Negin Masoudifar
Torsten Braun
Falko Dressler

BibTeX reference

@inproceedings{memedi2026towards,
    author = {Memedi, Agon and Masoudifar, Negin and Braun, Torsten and Dressler, Falko},
    note = {to appear},
    title = {{Towards Generalizable RL-Based Task Offloading for Vehicular Edge Computing}},
    publisher = {IEEE},
    address = {Montr{\'{e}}al, Canada},
    booktitle = {17th IEEE Vehicular Networking Conference (VNC 2026)},
    month = {6},
    year = {2026},
   }
   
   

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Last modified: 2026-04-21