Industrial edge computing task offloading framework driven by multimodal generative adversarial imitation learning

H. K. Huang

doi:10.1080/17517575.2026.2643633

Industrial edge computing task offloading framework driven by multimodal generative adversarial imitation learning

2026-03-18

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DOI：10.1080/17517575.2026.2643633

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Abstract

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Addressing the pain points of difficulty in obtaining expert strategies and insufficient generalisation of imitation learning in dynamic industrial edge environments, this paper proposes computational task offloading framework driven by multimodal generative adversarial imitation learning (GAIL-TO). GAIL-TO learns optimal strategy features from suboptimal historical logs through the adversarial mechanism of generator-discriminator, designs a spatiotemporal feature fusion encoder; constructs a lightweight adversarial training architecture, adapting to resource-constrained devices; develops an edge collaborative training mechanism, utilising server computing power to perform adversarial training, with only lightweight generators deployed at the terminal. It demonstrates superior performance compared to standard IL and MADDPG.

Keywords:

Multimodal

generative adversarial imitation learning

industrial edge computing

task offloading

framework

Industrial edge computing task offloading framework driven by multimodal generative adversarial imitation learning

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