Attention-Based Deep Reinforcement Learning for Joint Trajectory Planning and Task Offloading in AAV-Assisted Vehicular Edge Computing
Bibliographic record
Abstract
The rapid growth of compute-intensive and delay-sensitive applications in the Internet of Vehicles (IoV) has brought new challenges to traditional Vehicle Edge Computing (VEC). Especially during peak traffic periods, fixed base stations (BSs), due to their static deployment and limited coverage, struggle to elastically scale computing resources to meet the dynamically changing computational demands of vehicular users, which to some extent affects the overall quality of service and efficiency of system resource utilization. In this paper, we propose a hybrid edge computing framework that integrates Autonomous Aerial Vehicles (AAVs) as mobile computing nodes to complement fixed base stations. For this AAV-BS hybrid edge computing environment, we design and optimize a computation offloading model considering heterogeneous task types and AAV trajectory planning. The proposed model tackles the joint optimization of system revenue, service delay, and energy consumption, while considering practical constraints such as AAV energy limitations and base station capacity. To address this multi-objective optimization challenge, we construct a Markov Decision Process (MDP) model and develop a multi-head self-attention enhanced Multi-Agent Deep Dirichlet Deterministic Policy Gradient (MHSA-MAD3PG) algorithm. The multi-head self-attention mechanism enables agents to capture complex dependencies and interactions in the environment, leading to more effective collaborative decision-making. Comprehensive simulation results demonstrate that our proposed approach achieves superior performance in terms of system revenue, service delay, and energy efficiency compared to baseline methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".