Closed-Loop Optimal Freeway Ramp Metering Using Continuous State Space Reinforcement Learning with Function Approximation
Bibliographic record
Abstract
In recent years, Reinforcement Learning (RL), an Artificial Intelligence based learning method, has gained some interest among researchers in solving control systems problems. Although RL methods have been applied to different transportation problems such as ramp metering and traffic signal control, RL in its conventional form, with discrete state space representation, lacks learning efficiency and becomes intractable when applied to medium and large-scale transportation control problems. Continuous state space representation in RL problems implies direct representation of the problem’s continuous variables using function approximation techniques that has the potential to addresses some of the challenges associated with employing RL in large transportation networks. Function approximation methods, when properly designed, have the potential to result in 1) faster learning, 2) better performance, and 3) easier design/set up for RL control systems. In this paper, three function approximation techniques: k-nearest neighbor weighted average, multi-layer perception neural network, and linear model tree are developed and compared against the conventional table-based RL as a benchmark. The four approaches are applied to a ramp metering case study in the city of Toronto. The approaches are tested on a microsimulation model and compared using the following criteria: learning speed, design effort, computational requirements, and network performance. It is concluded that, for RL problems, the linear model tree method provides the best function approximation with minimal design effort given the noisy measurements in traffic control applications with more than 10 times faster leaning speed over the conventional table-based RL methods.
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 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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".