-| `road_traffic.py` | This scenario provides a MARL benchmark for Connected and Automated Vehicles (CAVs) using a High-Definition (HD) map from the Cyber-Physical Mobility Lab ([CPM Lab](https://cpm.embedded.rwth-aachen.de/)), an open-source testbed for CAVs. The map features an eight-lane intersection and a loop-shaped highway with multiple merge-in and -outs, offering a range of challenging traffic conditions. Forty loop-shaped reference paths are predefined, allowing for simulations with infinite durations. You can initialize up to 100 agents, with a default number of 20. In the event of collisions during training, the scenario reinitializes all agents, randomly assigning them new reference paths, initial positions, and speeds. This setup is designed to simulate the unpredictability of real-world driving. Besides, the observations are designed to promote sample efficiency and generalization (i.e., agents' ability to generalize to unseen scenarios). In addition, both ego view and bird's-eye view are implemented; partial observation is also supported to simulate partially observable Markov Decision Processes. See [this paper](http://dx.doi.org/10.13140/RG.2.2.24505.17769) for more info. | <img src="https://github.com/matteobettini/vmas-media/blob/main/media/scenarios/road_traffic_cpm_lab.gif?raw=true" alt="drawing" width="300"/> |
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