Network-aware multi-agent reinforcement learning for the vehicle navigation problem
Bibliographic record
Abstract
Traffic congestion is characterized by longer trip times, and increased air pollution. In a static road network, the travel time to a destination is constant and can be computed using the shortest path first algorithm (SPF). However, road network conditions are dynamic, rendering the SPF to perform sub-optimally at times. In addition, in a realistic multiple-vehicle scenario, the SPF routing algorithm can cause congestion by routing all vehicles through the same shortest path. In this paper, we propose a network-aware multi-agent reinforcement learning model for addressing this problem. Our key idea is to assign an RL agent to intersections. Each RL agent operates as a router agent and is responsible for providing routing instructions to approaching vehicles. When a vehicle reaches an intersection, it submits a routing query to the RL agent consisting of its final destination. The RL agent generates a routing response based on (i) the destination, (ii) the current state of the road network, and (iii) routing policies learned by cooperating with other neighboring RL agents. Our experimental evaluation shows that the proposed MARL model outperforms the SPF algorithm by (up to) 20.2% in average travel time.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".