A single-agent deep Q-learning model with MSA guidance sequentially assigns routes and approximates system-optimal traffic assignment on the Braess and OW networks within 0.35%.
Real-time system optimal traffic routing under uncertainties—can physics models boost reinforcement learning?
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Reinforcement Learning-based Sequential Route Recommendation for System-Optimal Traffic Assignment
A single-agent deep Q-learning model with MSA guidance sequentially assigns routes and approximates system-optimal traffic assignment on the Braess and OW networks within 0.35%.