Latency-Constrained Dynamic Computation Offloading in Mobile Edge Computing using Multi-Agent Reinforcement Learning
Bibliographic record
Abstract
Mobile edge computing (MEC) facilitates the development of compute-intensive and real-time applications on mobile devices by providing computing resources in proximity of users. To take full advantage of MEC, making optimal offloading decisions is critical. In this paper, we study the computation offloading of latency-constrained tasks from multiple users to an edge server under a stochastic environment with time-varying wireless channels and dynamically variable set of active users. To lower the mutual interference experienced by users while accessing a set of shared channels, we utilize game theory to formulate the decision-making process of users as a general-sum Markov game model. Then, we provide a mathematical proof to demonstrate the equivalency of the proposed game model to a weighed potential game, which guarantees the presence of at least one pure-strategy Nash Equilibrium (NE) point due to the finite improvement property. Next, we design multi-user computation offloading algorithms using a NE-based multi-agent reinforcement learning (MARL) technique to achieve the equilibrium solution of the game in a decentralized manner. Numerical results show that the proposed algorithms can effectively improve the convergence rate and greatly reduce the system-wide energy cost, outperforming the previously studied learning-based multi-agent algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".