Large-Sized Network: Independent AV agents

In this tutorial, we use a large network for agent navigation. The chosen origin and destination points are specified in this file and can be adjusted by users. We implement the learning process of the automated vehicles (AVs) using the TorchRL library.

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Network Overview

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

Users can customize TrafficEnvironment by consulting its current defaults.json, overriding the desired values in a dictionary, and passing that dictionary to the class.

Included Tutorials:

• IQL Tutorial. Uses Independent Q-Learning (IQL) (IQL) as an initial baseline for training decentralized policies.

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

• ISAC Tutorial. Uses Independent SAC (ISAC), the multi-agent extension of Soft Actor-Critic (SAC) (SAC), which balances exploration and exploitation using entropy-regularized reinforcement learning.

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Ingolstadt Network Visualization