Real-time Optimal Resource Allocation in Multiuser Mobile Edge Computing in Digital Twin Applications with Deep Reinforcement Learning : (Invited Paper)
Why this work is in the frame
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Bibliographic record
Abstract
We investigate the optimal resource allocation of mobile edge computing (MEC) with multiple Internet-of-Thing (IoT) devices in digital twin applications. Based on Markov decision process and model-free deep reinforcement learning (DRL) approach, we propose double deep RL-based online computation offloading method to implement the deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. In particular, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading task. The proposed real-time Lyapunov optimization and DRL algorithms achieve a low computational complexity and optimal processing time. Simulation results demonstrate that the proposed method can achieve near-optimal control performance with an enhanced energy consumption and significantly minimize the computation time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| 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