RouteRLΒΆ
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+RouteRL provides a Multi-Agent Reinforcement Environment (MARL) for urban route choice in different city networks.
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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.
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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.
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+It is compatible with popular RL libraries such as stable-baselines3 and TorchRL.
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For more details, check the documentation online.
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