Deep Learning vs. Discrete Reinforcement Learning for Adaptive Traffic Signal Control
Bibliographic record
Abstract
The population in cities and demand for transportation continuously increases. Space, financial and environmental constraints do not allow for significant infrastructure expansion. Therefore, optimizing the efficiency of the infrastructure is becoming increasingly important. Wait time at traffic lights is a significant proportion of time spent travelling within cities. Time inefficiency of traffic lights is, therefore, a global concern. Adaptive traffic signal controllers aim to provide demand-responsive strategies to minimize motorists' delay and achieve higher throughputs at signalized intersections. With the advent of new sensory technologies and more intelligent control methods, we propose an adaptive traffic signal controller able to receive un-prepossessed high-dimensional sensory information and self-learn to minimize the intersection delay. We use (1) deep neural networks to operate directly on detailed sensory inputs and feed it into (2) a continuous reinforcement learning based optimal control agent. The integration of the two is known as deep reinforcement learning or deep learning for short. Using deep learning, we achieve two goals: (1) eliminate the need for handcrafting a feature extraction process such as determining queue lengths for instance, which is challenging and location specific, and (2) achieve better performance and faster training time compared to conventional discrete reinforcement learning approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".