Guidelines for Parameter Selection in Traffic Light Control Methods Using Reinforcement Learning: Insights from Empirical Studies
Bibliographic record
Abstract
The ever-changing traffic dynamics make the traditional traffic signal control methods unable to adapt to the environment. Meanwhile, deep reinforcement learning (DRL) has the property of interacting with the environment and adapting to changes in the environment. Therefore, in recent years, researchers have usually solved traffic signal control (TSC) problems through DRL methods. They have not only improved the design of neural networks, but also improved the ability of models to understand traffic conditions and learn corresponding task requests by designing different states and rewards. However, although the existing TSC algorithms based on DRL have proposed many well-designed states and reward strategies, which combinations of states and rewards should be adopted in practice to achieve the performance margin of models remains a question that researchers are seeking the answer to. Therefore, we introduce a general simulation platform to test and compare experimental performance under different combinations of states and rewards. Specifically, we test and analyze the experimental effects under different combinations of multiple traffic states and rewards through various TSC methods with a set of unified model settings. We further design and test some new state representations and reward strategies based on more detailed traffic information. The test results show that when researchers design the state and reward, refining the traffic state like vehicle running condition and making the state and reward match can make the experimental performance better than other combinations in most cases. We hope these results have some implications for the state and reward choice when researchers conduct experiments on TSC problem or other traffic decision management problems.
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 | 0.029 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".