DTA-RL: Dynamic Topology Adaptive Reinforcement Learning Approach for Task Offloading in Mobile Edge Computing
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Bibliographic record
Abstract
Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it