Multi-objective task offloading for highly dynamic heterogeneous Vehicular Edge Computing: An efficient reinforcement learning approach

TL;DR: This study proposes HMO-SAC, a hybrid multi-objective reinforcement learning algorithm for adaptive edge task offloading in 6G networks, improving convergence speed by 14% and reducing task completion time and energy consumption by 23% compared to state-of-the-art methods.

TL;DR: This paper proposes P-MADDPG, a vehicular edge computing offloading algorithm that integrates trajectory prediction and multi-agent deep reinforcement learning to optimize power allocation, channel use, and computing resources, reducing delay and improving resource utilization.

Abstract: Object detection is a critical technology for smart city development. As request volumes surge, inference is increasingly offloaded from centralized clouds to user-proximal edge sites to reduce latency and backhaul traffic. However, heterogeneous workloads, fluctuating bandwidth, and dynamic device capabilities make offloading and scheduling difficult to optimize in edge environments. Deep reinforcement learning (DRL) has proved effective for this problem, but in practice, it relies on manually engineered reward functions that must be redesigned whenever service objectives change. To address this limitation, we introduce an LLM-driven framework that retargets DRL policies for edge object detection directly through natural language instructions. By leveraging understanding of the text and encoding capabilities of large language models (LLMs), our system (i) interprets the current optimization objective; (ii) generates an executable, environment-compatible reward function code; and (iii) iteratively refines the reward via closed-loop simulation feedback. In simulations for a real-world dataset, policies trained with LLM-generated rewards adapt from prompts alone and outperform counterparts trained with expert-designed rewards, while eliminating manual reward engineering.

TL;DR: Experimental results have demonstrated that MOEA/D with simple decomposition methods outperforms or performs similarly to MOGLS and NSGA-II on multiobjective 0-1 knapsack problems and continuous multiobjectives optimization problems.

TL;DR: In this article, the authors show that the DQN algorithm suffers from substantial overestimation in some games in the Atari 2600 domain, and they propose a specific adaptation to the algorithm and show that this algorithm not only reduces the observed overestimations, but also leads to much better performance on several games.

TL;DR: In this paper an extensive, quantitative comparison is presented, applying four multiobjective evolutionary algorithms to an extended 0/1 knapsack problem.

TL;DR: This paper explores a vehicle edge computing network architecture in which the vehicles can act as the mobile edge servers to provide computation services for nearby UEs and proposes as vehicle-assisted offloading scheme for UEs while considering the delay of the computation task.