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%.
Bridging the user equilibrium and the system optimum in static traffic assignment: a review,
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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%.