Sample-Efficient Meta-RL for Traffic Signal Control
Bibliographic record
Abstract
Traffic congestion is a pervasive challenge in urban areas, contributing significantly to greenhouse gas emissions. Efficient traffic signal control stands as a pivotal factor in enhancing modern transportation systems. The complexity of this decision-making process is heightened by the dynamic nature of traffic patterns. Reinforcement Learning (RL) approaches have showcased promise in addressing traffic signal control, exhibiting notable performance gains over traditional methods. However, the practical utility of many RL-based solutions is constrained by their substantial data requirements, limiting applicability to real-world scenarios. This paper introduces an innovative model-based meta-reinforcement learning framework, ModelLight, designed for traffic signal control. In ModelLight, world models capturing the dynamics of signalized intersection are acquired and employed to generate imaginary trajectories within an optimization-based meta-learning approach, thereby enhancing overall sample efficiency. Experimental evaluations on real-world scenarios demonstrate that ModelLight surpasses RL-based traffic signal control baselines while demanding only a fraction of the interactions with the environment. Our datasets and code can be found at https://github.com/XingshuaiHuang/ModelLight.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".