RouteRL

RouteRL provides a Multi-Agent Reinforcement Environment (MARL) for urban route choice in different city networks.

• The main class is TrafficEnvironment and is a PettingZoo AEC API environment.

• There are two types of agents in the environment and are both represented by the BaseAgent class.

  • Human drivers are simulated using human route-choice behavior from transportation research.

  • Automated vehicles (AVs) are the RL agents that aim to optimize their routes and learn the most efficient paths.

• It is compatible with popular RL libraries such as stable-baselines3 and TorchRL.

For more details, check the documentation online.