Optimizing the Junction-Tree-Based Reinforcement Learning Algorithm for Network-Wide Signal Coordination
Bibliographic record
Abstract
This study develops three measures to optimize the junction-tree-based reinforcement learning (RL) algorithm, which will be used for network-wide signal coordination. The first measure is to optimize the frequency of running the junction-tree algorithm (JTA) and the intersection status division. The second one is to optimize the JTA information transmission mode. The third one is to optimize the operation of a single intersection. A test network and three test groups are built to analyze the optimization effect. Group 1 is the control group, group 2 adopts the optimizations for the basic parameters and the information transmission mode, and group 3 adopts optimizations for the operation of a single intersection. Environments with different congestion levels are also tested. Results show that optimizations of the basic parameters and the information transmission mode can improve the system efficiency and the flexibility of the green light, and optimizing the operation of a single intersection can improve the efficiency of both the system and the individual intersection. By applying the proposed optimizations to the existing JTA-based RL algorithm, network-wide signal coordination can perform better.
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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".