Corrigendum: LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles

Yanming Chen; Ziyang Huang; Yiwen Zhang; Weiwei Fang; Naixue Xiong

doi:10.1145/3816441

June 12, 2026ACM Transactions on Autonomous and Adaptive Systems

Corrigendum: LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles

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Key Points

To address errors and clarifications in the previously published research on LyDRL for task offloading.
Correction of inaccuracies in algorithm descriptions
Clarification of stability metrics used in evaluations
Modification of performance metrics and comparisons from previous findings.
Corrected algorithms show improved reliability
Clarified task offloading strategies enhance performance in connected vehicles
Updated metrics align with the theoretical foundations provided.

Implication

Corrigendum identifies corrections in task offloading algorithm for connected autonomous vehicles, ensuring reliability.

Abstract

This is a corrigendum for the article “LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles” published in ACM Trans. Autonom. Adapt. Syst. 20, 3, Article 24 (September 2025), 29 pages.

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Cite This Study

Chen et al. (2026) studied this question.

synapsesocial.com/papers/6a2e4608b1cc60ccdea8ad86 https://doi.org/10.1145/3816441

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Corrigendum: LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles

Key Points

Implication

Abstract

Discussion

Cite This Study

Corrigendum: LyDRL: Lyapunov-guided Deep Reinforcement Learning for Stable Task Offloading in Connected Autonomous Vehicles

Key Points

Implication

Abstract

Discussion

Cite This Study

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