We model a transportation network where agents of different types operate with conflicting objectives: drivers want to drive at high speeds to reach their destinations faster, while police units want to prevent unlawful speeding. Police units have to efficiently allocate their limited resources to monitor roads and catch speeders, who try to avoid being caught. Assuming that police and drivers make strategic choices, the problem can be modeled using game theory. We describe the models and algorithms we developed and validate them on synthetic and real traffic data from different maps.
- Adversarial reinforcement learning
- Experience-Weighted Attraction
- Game theory
- Stochastic game