Cooperative Variable Speed Limit Control using Multi-agent Reinforcement Learning and Evolution Strategy for Improved Throughput in Mixed Traffic
Bibliographic record
Abstract
Improving the traffic throughput in mixed traffic scenarios including both human-driving vehicles and Connected and Automated Vehicles (CAVs) has long been a hot spot in automated driving. In recent years, variable speed limit (VSL) has been a promising solution and attracts considerable attention from both industry and academy. In this paper, a multi-agent reinforcement learning model and evolution strategy-based approach is proposed to provide both macroscopic and microscopic control in mixed traffic scenarios. In this approach, Graph Attention Networks (GATs) are introduced into Deep Q-Networks for vehicles' decision making. The architecture of the VSL network is designed using an evolution strategy to provide real-time speed limit. A dedicated reward function has been implemented to consider both the actions and speed limit. Extensive experiments are conducted focusing on Bottleneck networks. The experimental results show that the proposed approach has demonstrated superior performance compared with other baselines in terms of several metrics such as throughput, average speed, and safety.
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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".