A Case for Monte Carlo Tree Search in Adaptive Traffic Signal Control: Modifiability, Interpretability and Generalization
Bibliographic record
Abstract
Adaptive Traffic Signal Control methods based on Reinforcement Learning have been applied successfully to reach state-of-the-art results in simulation. However, most recent works in the area use model-free methods, which learn value and/or policy functions directly through environment in-teractions without a dynamics model. Model-free methods have several potential drawbacks: (1) difficulties in generalization to dynamics not seen in training, as well as difficulties in transfer to different intersection geometries; (2) low sample-efficiency, resulting in high resource requirements for training; (3) often, reliance on difficult-to-interpret function approximators such as neural networks. In this work, we demonstrate experimentally that Monte Carlo Tree Search (MCTS) with a simple queue and platoon-propagation dynamics model can reach lower delay on a single intersection than the standard model-free Deep Q-Networks (DQN) algorithm. Moreover, we provide empirical results that illustrate that model-based receding-horizon planning can alleviate many of the drawbacks of the model-free methods listed above: better generalization characteristics; more direct and less resource-demanding transfer to different intersection geometries and phasing schemes; and simpler visualization and interpretation, aiding modification and debugging.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".