RouteRL is a multi-agent reinforcement learning framework that integrates RL-based collective route choice with a microscopic traffic simulation, SUMO, facilitating the testing and development of efficient route choice strategies. The proposed framework simulates the daily route choices of driver agents in a city, including two types:

• human drivers, emulated using discrete choice models,

• and AVs, modeled as MARL agents optimizing their policies for a predefined objective.

RouteRL aims to advance research in MARL, traffic assignment problems, social reinforcement learning, and human-AI interaction for transportation applications.

• 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.

• RouteRL is compatible with popular RL libraries such as TorchRL.