Multi‐Intersection Signal Control Based on Asynchronous Reinforcement Learning
Bibliographic record
Abstract
State‐of‐the‐art theoretical models and new traffic signal control technologies are key guarantees for improving the management and safety performance of transportation systems, and multiagent reinforcement learning (MARL) methods have been widely applied in the field of signal control. Researchers in the transportation domain have effectively addressed the issues of poor convergence and suboptimal optimization encountered in RL for multi‐intersection signal control scenarios by adopting the centralized training with decentralized execution (CTDE) approach. However, due to the heterogeneity among intersections, simply decomposing the global reward into a sum of intersection‐level rewards is unreasonable, posing a challenge in balancing the interests of individual intersections and the entire road network. Additionally, the assumption that all intersections within the system make decisions synchronously is rather strong. Therefore, this paper proposes a distributed traffic model tailored for synchronous decision‐making and, based on that, introduces an asynchronous decision‐making traffic model according to decoupled intersection control. Simulation experiments show that the asynchronous decision‐making method proposed in this paper not only improves the model convergence speed by at least 19% compared to the multiagent deep RL (MADRL) algorithm used for synchronous decision‐making, but also improves the model by at least 10.5% in vehicle driving speed, maximum queue length, and average queue length within the decodable range (the traffic density is between 100 vehicles/km and 400 vehicles/km). In the same traffic scenario, the MADRL algorithm used for asynchronous decision‐making has improved the average vehicle delay and average queue length by at least 55% compared to traditional arterial green wave control methods and adaptive control methods, and by at least 5% compared to SAC and A2C methods.
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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 | 0.001 |
| 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".