Resource Allocation for Dynamic Platoon Digital Twin Networks: A Multi-Agent Deep Reinforcement Learning Method
Bibliographic record
Abstract
Vehicle driving in a platoon is an efficient and ecological driving solution. Introducing the concept of digital twin (DT) into the platoon to establish platoon digital twin (PDT) can improve the management efficiency and driving safety of the platoon. However, the joint allocation of multiple types of resources in a platoon digital twin network (PDTN) is an important issue for the successful implementation and maintenance of the PDT. In this paper, we investigate the resource allocation problem in a PDTN. By comprehensively considering the effects of high mobility of platooning vehicles, real-time nature of the DTs, and multi-vehicle cooperation, we propose a PDT utility optimization model for bandwidth and computation resource allocation. We formulate the dynamic resource allocation problem as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$M$</tex-math></inline-formula>-th order Markov decision process (MDP) and design a deep reinforcement learning (DRL)-based dynamic resource allocation (DRLDRA) method to solve it. To optimize the actions of the agent, we reshape the state in a smaller time granularity to better reflect the temporal variations of the state. Correspondingly, we design temporal feature extraction neural networks (TFENNs) based on multi-head self-attention (MHSA) mechanism and long short-term memory (LSTM) to extract the temporal features of the state. To improve the learning efficiency, a decentralized multi-agent deep deterministic policy gradient (DDPG)-based learning framework is proposed. Numerical results show that the DRLDRA method performs excellently and outperforms other benchmark 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 | 0.000 | 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 | 0.000 | 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".