GAN and Multi-Agent DRL Based Decentralized Traffic Light Signal Control
Bibliographic record
Abstract
Adaptive traffic light signal control (ATSC) is a promising paradigm for alleviating traffic congestion in intelligent transportation systems. Most of the existing methods require heavy traffic data exchange among neighboring intersections to achieve collaborative ATSC, which may not be supported by bandwidth-limited communication links in practice. In this article, we develop a communication-efficient decentralized ATSC framework for traffic networks with multiple intersections, where each intersection only exchanges traffic statistics with its neighboring intersections. In particular, the proposed framework consists of a generative adversarial network (GAN) based algorithm for traffic data recovery, and a multi-agent deep reinforcement learning (DRL) based decentralized ATSC algorithm for traffic efficiency enhancement. By adopting the value decomposition technique that establishes a nonlinear mapping from the local state-action values to the global reward, each intersection can independently determine its traffic light signal based on its local traffic data while achieving collaboration among neighboring intersections. Our proposed decentralized ATSC framework is scalable to large-scale traffic networks, and is also robust to traffic flow variations via interacting with the environment. Simulations show that our proposed algorithm can significantly reduce the vehicle travel time while maintaining high and stable traffic throughput.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".