Diffusion-Based Deep Reinforcement Learning for Resource Management in Connected Construction Equipment Networks: A Hierarchical Framework
Bibliographic record
Abstract
With the extensive adoption of information technology, tunnel construction is experiencing a rapid digital transformation. Integrating powerful direct communication among construction equipment (CE) facilitates real-time data exchange, promoting collaborative operations among CE. Concurrent execution of multiple construction procedures leads to a significant rise in the amount of CE and communication links, resulting in resource competition. However, this competition is aimed at enhancing collaboration. To address this inherently contradictory issue, we propose a hierarchical resource management framework and align communication quality of service (QoS) to construction efficiency using construction procedure coherence degree (CPCD) based on age of information (AoI). By formulating resource management as a stochastic optimization problem, a suitable online two-level deep reinforcement learning algorithm referred to as diffusion based soft actor critic (DSAC)-QMIX is designed to derive the radio resource allocation strategies. DSAC is responsible for orchestrating spectrum inter-fleets at the high-level, and QMIX makes the resource management and power control decision for each CE at the low-level. Simulation results validate the effectiveness of the DSAC-QMIX algorithm with comparable transmission rate, and show superior performance in terms of CPCD satisfaction compared with other benchmarks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".