GAT-MF: Graph Attention Mean Field for Very Large Scale Multi-Agent Reinforcement Learning (KDD'23)

To run the grid-world experiments, you need:

• code/grid_simulator/: simultor for the grid-world experiment
• code/grid_networks.py: neural networks definition for the grid-world experiment
• code/grid_train.py: run this Python file to execute the training
• model/: trained models will be stored to this folder

To run the real-world experiments, you need:

• data/: please download the required data (~10GB) from https://drive.google.com/drive/folders/1-68jPOd6NXVyiC1PWbo-9wrqiktOi4GT?usp=sharing and substitute this folder
• code/simulator/: simultor for the real-world experiment
• code/networks.py: neural networks definition for the real-world experiment
• code/constants.py: intrinsic constants for the real-world experiment
• code/data_range.py: data ranges for normalizations in the real-world experiment
• code/reward_fun.py: reward function definition for the real-world experiment
• code/util.py: util functions for the real-world experiment
• code/train.py: run this Python file to execute the training
• model/: trained models will be stored to this folder