Large-Sized Network: Independent AV agents

In this tutorial we use a big-scale netowk for agents navigation. The chosen origin and destination points are specified in this file, and can be adjusted by users. In parallel, we define AV behaviors based on the agents’ reward formulation and implement their learning process using the TorchRL library.

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Network Overview

In these notebooks, we utilize the Manhattan network within our simulator, SUMO. Since agents exhibit selfish behavior, we employ independent learning algorithms to model their decision-making.

Users can customize parameters for the TrafficEnvironment class by consulting the routerl/environment/params.json file. Based on its contents, they can create a dictionary with their preferred settings and pass it as an argument to the TrafficEnvironment class.

Included Tutorials:

• IPPO Tutorial
  Implements Independent Proximal Policy Optimization (IPPO) (IPPO), which has demonstrated strong benchmark performance in various tasks (paper1, paper2).

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Manhattan Network Visualization