eMARLIN: Distributed Coordinated Adaptive Traffic Signal Control with Topology-Embedding Propagation
Bibliographic record
Abstract
In this paper, we examine the practical problem of minimizing the delay in traffic networks that are controlled at each intersection independently, without a centralized supervisory computer and with limited communication bandwidth. We find that existing learning algorithms have lackluster performance or are too computationally complex to be implemented in the field. Instead, we introduce a simple yet efficient and effective approach using multi-agent reinforcement learning (MARL) that applies the Deep Q-Network (DQN) learning algorithm in a fully decentralized setting. First, we decouple the DQN into per-intersection Q-networks and then transmit the output of each Q-network’s hidden layer to its intersection neighbors. We show that our method is computationally efficient compared with other MARL methods, with minimal additional overhead compared with a naive isolated learning approach with no communication. This property enables our method to be implemented in real-world scenarios with less computation power. Finally, we conduct experiments for both synthetic and real-world scenarios and show that our method achieves better performance in minimizing intersection delay than other methods.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".