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.